http://orcid.org/0000-0001-9755-4162
Figures
Abstract
Over the past few years, the field of visual social cognition and face processing has been dramatically impacted by a series of data-driven studies employing computer-graphics tools to synthesize arbitrary meaningful facial expressions. In the auditory modality, reverse correlation is traditionally used to characterize sensory processing at the level of spectral or spectro-temporal stimulus properties, but not higher-level cognitive processing of e.g. words, sentences or music, by lack of tools able to manipulate the stimulus dimensions that are relevant for these processes. Here, we present an open-source audio-transformation toolbox, called CLEESE, able to systematically randomize the prosody/melody of existing speech and music recordings. CLEESE works by cutting recordings in small successive time segments (e.g. every successive 100 milliseconds in a spoken utterance), and applying a random parametric transformation of each segment’s pitch, duration or amplitude, using a new Python-language implementation of the phase-vocoder digital audio technique. We present here two applications of the tool to generate stimuli for studying intonation processing of interrogative vs declarative speech, and rhythm processing of sung melodies.
Citation: Burred JJ, Ponsot E, Goupil L, Liuni M, Aucouturier J-J (2019) CLEESE: An open-source audio-transformation toolbox for data-driven experiments in speech and music cognition. PLoS ONE 14(4): e0205943. https://doi.org/10.1371/journal.pone.0205943
Editor: Psyche Loui, Northeastern University, UNITED STATES
Received: September 30, 2018; Accepted: February 15, 2019; Published: April 4, 2019
Copyright: © 2019 Burred et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data and analysis scripts have been uploaded as supplemental material. Toolbox available open-source at http://forumnet.ircam.fr/product/cleese.
Funding: The funder (H2020 European Research Council) provided funding for the project to J-J.A (Award Number CREAM - 335536), but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author J.J.B., affiliated as Independent Researcher, is registered as a sole-proprietorship company in France dedicated to research and development consulting. Its participation to this study was fully funded by the above-mentioned grant.
Competing interests: Author J.J.B., affiliated as Independent Researcher, is registered as a sole-proprietorship company in France dedicated to research and development consulting. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Author J.J.B. declares that there is no competing interest, financial or otherwise, related to the publication of this study.
Introduction
The field of high-level visual and auditory research is concerned with the sensory and cognitive processes involved in the recognition of objects or words, faces or speakers and, increasingly, of social expressions of emotions or attitudes in faces, speech and music. In traditional psychological methodology, the signal features that drive judgments (e.g., facial metrics such as width-to-height ratio, acoustical features such as mean pitch) are posited by the experimenter before being controlled or tested experimentally, which may create a variety of confirmation biases or experimental demands. For instance, stimuli constructed to display western facial expressions of happiness or sadness may well be recognized as such by non-western observers [1], but yet may not be the way these emotions are spontaneously produced, or internally represented, in such cultures [2]. Similarly in auditory cognition, musical stimuli recorded by experts pressed to express emotions in music may do so by mimicking expressive cues used in speech, but these cues may not exhaust the many other ways in which arbitrary music can express emotions [3]. For all these reasons, in recent years, a series of powerful data-driven paradigms (built on techniques such as reverse-correlation, classification image or bubbles; see [4] for a review) were introduced in the field of visual cognition to discover relevant signal features empirically, by analyzing participant responses to large sets of systematically-varied stimuli [5].
The reverse correlation technique was first introduced in neurophysiology to characterize neuronal receptive fields of biological systems with so-called “white noise analysis” [6–9]. In psychophysics, the technique was then adapted to characterize human sensory processes, taking behavioral choices (e.g., yes/no responses) instead of neuronal spikes as the systems’ output variables to study, e.g. in the auditory domain, detection of tones in noise [10] or loudness weighting in tones and noise ([11]; see [4] for a review of similar applications in vision). In the visual domain, these techniques have been extended in recent years to address not only low-level sensory processes, but higher-level cognitive mechanisms in humans: facial recognition [12], emotional expressions [2, 13], social traits [14], as well as their associated individual and cultural variations ([15]; for a review, see [5]). In speech, even more recently, reverse correlation and the associated “bubbles” technique were used to study spectro-temporal regions underlying speech intelligibility [16, 17] or phoneme discrimination in noise [18, 19] and, in music, timbre recognition of musical instruments [20, 21].
All of these techniques aim to isolate the subspace of feature dimensions that maximizes participant responses, and as such, need to search the stimulus generative space e.g. of all possible images or sounds relevant for a given task. A typical way to define and search such a space is to consider a single target stimulus (e.g. a neutral face, or the recording of a spoken phoneme), apply a great number of noise masks that modify the low- or high-level physical properties of that target, and then regress the (random) physical properties of the masks on participant judgements. Techniques differ in how such noise masks are generated, and on what stimulus subspace they operate (Fig 1). At the lowest possible level, early proposals have applied simple pixel-level noise masks [13], and sometimes even no stimulus at all, akin to white noise “static” on a TV screen in which participants were forced to confabulate the presence of a visual target [22] or a vowel sound [19]. More consistently, noise masks generally operate on low-level, frequency-based representations of the signal, e.g. at different scales and orientations for image stimuli [12, 14], different frequencies of an auditory spectrogram [17, 23] or different rates and scales of a modulation spectrum (MPS) [16, 21].
Reverse-correlation paradigms aim to isolate the subspace of feature dimensions that maximizes participant responses, and as such, need to search a stimulus generative space e.g. of all possible images or sounds relevant for a task. In a majority of studies, noise masks operate on low-level, frequency-based representations of the signal, e.g. at different scales and orientations for image stimuli (top-left) or different frequencies of an auditory spectrogram (top-right). More recent models in the vision modality are able to explore the subspace of facial expressions through the systematic manipulation of facial action units (bottom-left). The present work represents a conceptually-similar development for the auditory modality.
While low-level subspace noise has the advantage of providing a physical description of the stimuli driving participant responses, it is often a suboptimal search space for high-level cognitive tasks. First, all stimulus features that are driving participant judgements may not be efficiently encoded in low-level representations: the auditory modulation spectrum, for instance, is sparse for a sound’s harmonic regularities, coded for by a single MPS pixel, but not for features localized in time, such as attack time or transitions between phonemes [23]. As a consequence, local mask fluctuations will create continuous variations for the former, but not the latter features which will be difficult to regress on. Second, low-level variations in stimuli typically create highly distorted faces or sounds (Fig 1-top), for which one may question the ecological relevance of participant judgements. Finally, and perhaps most critically, many of the most expressive, cognitively-meaningful features of a face or voice signal are coordinated action-units (e.g. a contraction of a facial muscle, the rise of pitch at the end of a spoken utterance) that have distributed representations in low-level search spaces, and will never be consistently explored by a finite amount of random activations at that level.
Consequently, the face research community has recently developed a number of higher-level generative models able to synthesize facial expressions through the systematic manipulation of facial action units [24, 25]. These tools are based on morphological models that simulate the ‘amplitude vs time’ effect of individual muscles and then reconstruct realistic facial textures that account for the modified underlying morphology, as well as head pose and lighting. Searching such high-level subspaces with reverse-correlation is akin to searching the space of all possible facial expressions (Fig 1-bottom), while leaving out the myriad of other possible facial stimuli that are not directly interpretable as human-made expressions—a highly efficient strategy that has been applied to characterize subtle aspects of social and emotional face perception processes, such as cultural variations in emotional expressions [2], physical differences between different kinds of smiles [26] or in the way these features are processed in time [27]. In auditory research, however, similarly efficient data-driven strategies have not yet been common practice, by lack of tools able to manipulate the high-level stimulus dimensions that are relevant for similar judgement tasks when they apply on, e.g., voice or music.
Here, we present an open-source audio-transformation toolbox, called CLEESE (Combinatorial Expressive Speech Engine, named after British actor John Cleese, with reference to the “Ministry of Silly Talks”), able to systematically randomize the prosody/melody of existing speech and music recordings. CLEESE works by cutting recordings in small successive time segments (e.g. every successive 100 milliseconds in a spoken utterance), and applying a random parametric transformation of each segment’s pitch, duration, amplitude or frequency content, using a new Python-language implementation of the phase-vocoder digital audio technique. Transformations made with CLEESE explore the space of speech intonation and expressive speech prosody by allowing to create random time-profiles of pitch (e.g. rising pitch at the end of an utterance, as in interrogative sentences [28]), of duration (e.g. word-final vowel lengthening, as used as a cue for word segmentation [29]) or amplitude (e.g. louder on prominent words or phonemes [30]). In music, the same transformations can be described as melodic, manipulating the pitch/tuning of successive notes in a sequence (e.g. ♩♭, ♩ or ♩♯), their duration (e.g. , ♩ or ♪) or amplitude (e.g. p, mf or f). All transformations are parametric, thus allowing to generate thousands of random variants drawn e.g. from a gaussian distribution; and realistic (within appropriate parameter ranges), such that the resulting audio stimuli do not typically appear artificial/transformed, but rather plausible as ecological speech or music recordings.
In recent work, we have used CLEESE with a reverse-correlation paradigm to uncover what mental representations of pitch profiles underlie judgements of speaker dominance and trustworthiness in short utterances like the word ‘hello’ [31]. We recorded a single utterance of the word ‘hello’ by one male and one female speaker. CLEESE was first used to flatten the pitch of the recordings (by transforming it with a pitch profile that alters its original prosody to constant pitch), and then to generate random pitch variations, by manipulating the pitch over 6-time points on Gaussian distributions of SD = 70 cents clipped at ±2.2 SD (these values were chosen in order to map natural variations in speech). Pairs of these randomly-manipulated voices were then presented to observers who were asked, on each trial, to judge which of the two versions appeared most dominant/trustworthy. The participants’ mental representations were then computed by computing the mean pitch contour (a 6-point vector) of the voices they classified as dominant (resp. trustworthy) minus the mean pitch contour of the voices classified as non-dominant (resp. non-trustworthy).
In this article, we describe the functionality of the CLEESE toolbox, the algorithms that underlie it as well as how to deploy it in psychophysical reverse-correlation experiments such as those above. We then present two case-studies in which we use CLEESE to generate pitch-shifted speech stimuli to study the perception of interrogative vs declarative speech prosody, and time-stretched musical stimuli to study the rhythm processing of sung melodies.
Functionality, algorithms, usage
Functionality
CLEESE is an open-source Python toolbox used to create random or fixed pitch, time and amplitude transformations on any input sound. CLEESE can also be used to create spectral transformations, a technique not described here—see [32]. The transformations can be both static or time-varying. Besides its purpose of generating many stimuli for reverse-correlation experiments, the toolbox can also be used for producing individual, user-determined transformations. CLEESE is available for download upon free registration at http://forumnet.ircam.fr/product/cleese.
CLEESE operates by generating a set of random breakpoint functions (BPFs) for each transformation, which are then passed to a spectral processing engine (based on the phase vocoder algorithm, see below) for the transformation to occur. BPFs determine how the sound transformations vary over the duration of the stimulus. CLEESE can randomly generate BPFs of two types: ramps, where the corresponding sound parameter is interpolated linearly between breakpoints (Fig 2-top) and square, where the BPF is a square signal with sloped transitions (Fig 2-bottom). BPF segments can be defined either by forcing all of them to have a fixed duration (i.e., their number will depend on the whole sound’s duration, see Fig 2-left), or by forcing a fixed number of segments along the total sound duration (i.e., their duration will depend on the whole sound’s duration, see Fig 2-right). Alternatively, the user can pass custom breakpoint positions, which can be defined manually e.g. to correspond to each syllable or note in given a recording.
In ramp BPFs (top), transformation parameters are interpolated linearly between breakpoints. In square BPFs, they are constant in each segment. BPFs can be specified either in terms of fixed duration (left: 80ms segments) or fixed number of segments (right: 6 segments).
BPFs can be defined to transform sounds along three signal dimensions:
- Pitch: The BPF is used to transpose up and down the pitch of each segment in the sound, while maintaining its amplitude and duration constant. Each breakpoint in the BPF corresponds to a pitch shift value p in cents or percents of a semitone, with 0 cents corresponding to no change with respect to original pitch. See S1 Sound for an example of pitch shifting manipulation.
- Time: The BPF is used to stretch or compress the duration of each segment in the sound, while maintaining its amplitude and pitch constant. Each breakpoint corresponds to a time stretch factor t in ratio to original duration, with 0 < t < 1 corresponding to compression/faster speed, t > 1 stretched/slower speed, and t = 1 no change with respect to segment’s original duration. See S2 Sound for an example of time stretching manipulation.
- Amplitude: The BPF is used to amplify or decrease the signal’s instantaneous amplitude in each segment, while maintaining its pitch and duration constant. Each breakpoint corresponds to a gain value g in dBs (thus, in base-10 logarithm ratio to original signal amplitude), with g = 0 corresponding to no change with respect to original amplitude.
The default mode is for CLEESE to generate p, t or g values at each breakpoint by sampling from a Gaussian distribution centered on p = 0, t = 1 or g = 0 (no change) and whose standard deviation (in cents, duration or amplitude ratio) allows to statistically control the intensity of the transformations. For instance, with a pitch SD of 100 cents, CLEESE will assign random shift values at every breakpoint, 68% of which are within ± 1 semitone of the segment’s original pitch (Fig 3A and 3B). The transformations can be chained, e.g. manipulating pitch, then duration, then amplitude, all with separate transformation parameters. For each type of transformation, distributions can be truncated (at given multiples of SD) to avoid extreme transformation values which may be behaviorally unrealistic. Alternatively, transformation parameters at each breakpoint can be provided manually by the user. For instance, this allows to flatten the pitch contour of an original recording, by constructing a custom breakpoint function that passes through the pitch shift values needed to shift the contour to a constant pitch value (Fig 3C and 3D).
A: pitch of an original male speech recording (a 400ms utterance of the word ‘hello’). B: Random pitch transformations of the original, where pitch shift values at each breakpoint are drawn from a Gaussian distribution. C: Manually-specified BPF created to flatten the pitch contour of the original recording. D: Resulting flat-pitch transformation, compared to original.
Algorithm
The phase vocoder is a sound processing technique based on the short-term Fourier Transform (STFT, [33]). The STFT decomposes each successive part (or frame) of an incoming audio signal into a set of coefficients that allow to perfectly reconstruct the original frame as a weighted sum of modulated sinusoidal components (Eq (1)). The phase vocoder algorithm operates on each frame’s STFT coefficients, modifying them either in their amplitude (e.g. displacing frequency components to higher frequency positions to simulate a higher pitch) or their position in time (e.g. displacing frames to later time points to simulate a slower sound). It then generates a modified time-domain signal from the manipulated frames with a variety of techniques meant to ensure the continuity (or phase coherence) of the resulting sinusoidal components [34, 35]. Depending on how individual frames are manipulated, the technique can be used e.g. to change a sound’s duration without affecting its pitch (a process known as time stretching), or to change a sound’s pitch without changing its duration (pitch shifting, see [36] for a review).
In more detail, the phase vocoder procedure considers a (digital) sound x as a real-valued discrete signal, and a symmetric real-valued discrete signal h composed of N samples, usually called window, that is used to cut the input sound into successive frames. In the analysis stage, frames are extracted with a time step (or hop size) Ra, corresponding to successive time positions , where u is the index of the u-th frame. The discrete STFT of x with window h is given by the Discrete Fourier Transform (DFT) of the time frames of x multiplied by h,
(1)
where
is the STFT coefficient corresponding to frequency
, k = 0, …, N − 1 in the u-th frame of the signal. X is a two-dimensional complex-valued representation, which can be expressed in terms of real and imaginary parts or, equivalently, amplitude and phase. The amplitude of the coefficient at time index u and frequency bin k is given by
, while its phase is
.
A given transformation T(X) = Y can then be performed by altering the amplitudes, phases or temporal positions of the frames, leading to a complex-valued representation Y of the same dimension as X. The transformed coefficients in Y are then used to synthesize a new sound y, using the inverse procedure to Eq (1). Using the same window function h and a synthesis hop size Rs such that , the output signal is given by
(2)
(3)
Time stretching is performed by modifying the synthesis hop size Rs with respect to the analysis hop size Ra. In CLEESE’s implementation, the analysis hop size Ra is changed according to the desired stretch factor t, while the synthesis hop size Rs is kept constant (the other way around is also possible). Pitch shifting with a factor p can be performed by either warping the amplitudes of each frame along the frequency axis, or by performing time stretching with the same factor p followed by resampling with the inverse factor 1/p to restore the original duration (CLEESE uses the latter method). Finally, amplitude manipulations by a time-frequency mask G composed of scalar gain factors gu,k can be generated by taking for the amplitudes, and leaving phases unchanged.
When STFT frames are time-shifted as part of the phase-vocoder transformation, the position of the u-th output frame is different from and the phases of the STFT coefficients have to be adapted to ensure the continuity of the reconstructed sinusoidal components and the perceptual preservation of the original sound’s timbre properties. Phase vocoder implementations offer several methods to this end [36]. In one classic procedure (horizontal phase synchronization), phases are adjusted independently in all frequency positions, with phases at position
extrapolated from phases at position
[34]. In an improved procedure (vertical phase synchronization, or phase-locking), each frame is first analysed to identify prominent peaks of amplitude along the frequency axis, and phases are only extrapolated in the peak frequency positions; phases of the frequency positions around each peak are locked to the phase of the peak frequency position [35]. It is this second procedure that is implemented in CLEESE.
Further artifacts are inherent to phase-vocoder transformations. For instance, the pitch shifting procedure also shifts the positions of the formants, which results in unnatural timbres for shifting factors of more than a few semitones. Several strategies have been devised in the literature to compensate for this formant shift, often based on the detection and preservation of the original spectral envelope of the input signal (a spectral envelope is a smooth curve that approximately follows the contour defined by the amplitude peaks of the sinusoidal partials in the spectrum). Another common artifact is transient smearing, which is the distortion and loss of definition of the transients when they are subjected to time stretching (transients are noisy, aperiodic parts of the sound, such as unvoiced consonants). This can be avoided by an additional step aimed at detecting the unvoiced portions of the signal and bypassing phase vocoder modifications on those segments. Neither envelope nor transient preservation are implemented in the current version of CLEESE, but their perceptual importance is deemed to be minimal in the usage scenario discussed here, which involves small random variations of the pitch and stretch factors.
Usage
CLEESE is implemented as module for the Python (versions 2 and 3) programming language, and distributed under an open-source MIT License. It provides its own implementation of the phase vocoder algorithm, with its only dependencies being the numpy and scipy libraries.
In its default ‘batch’ mode, CLEESE generates many random modifications from a single input sound file, called the base sound. It can be launched as follows:
import cleese
inputFile = ‘path_to_input_sound.wav’
configFile = ‘path_to_config_file.py’
cleese.process(soundData = inputFile, configFile = configFile)
where inputFile is the path to the base sound (a mono sound in WAV format) and configFile is the path to a user-generated configuration script containing generation parameters for all transformations. For each run in batch mode, the toolbox generates an arbitrary number of random BPFs for each transformation, applies them to the base sound, and saves the resulting files and their parameters.
The main parameters of the configuration file include how many files should be generated, where the transformed files should be saved, and what transformation (or combinations thereof) should be applied. For instance, the following configuration file generates 10 audio files which result from a random stretch, followed by a random pitch transformation of the base sound.
# main parameters
main_pars = {
‘numFiles’: 10, # number of output files to generate
‘outPath’: ‘/path_to_output_folder/’, # output root folder
‘chain’: True, # apply transformations in series (True)
# or parallel (False)
‘transf’: [‘stretch’, ‘pitch’] # modifications to apply
}
In addition, the configuration file includes parameters that specify how BPFs should be generated for each transformation, including the number or duration of BPF windows, their type (ramp or square) and the standard deviation of the gaussian distribution used to sample breakpoints. As an example, the following parameters correspond to pitch BPFs consisting of 6 ramp windows, each with a normally distributed pitch shift between -300 and 300 cents.
# pitch transformation parameters
pitch_pars = {
‘winLen’: 0.11, # BPF window (sec)
# if 0: static transformation
‘numWin’: 6, # number of BPF windows.
# if 0: static transformation
‘winUnit’: ‘n’, # ‘s’: force winLen in seconds,
# ‘n’: force number of windows
‘std’: 300, # standard deviation for each breakpoint
‘trunc’: 1, # truncate distribution (factor of std)
‘BPFtype’: ‘ramp’, # type of breakpoint function:
# ‘ramp’: linear interpolation between breakpoints
# ‘square’: square BPF
‘trTime’: 0.02 # transition time for square BPF (sec)
}
The CLEESE module is distributed with a Jupyter notebook tutorial, showing further examples of using the toolbox for sound manipulation. In addition, all experimental data and analysis scripts (also in the form of Jupyter notebooks) from the following two case-studies are made available as supplementary material.
Case-study (1): Speech intonation
To illustrate the use of CLEESE with speech stimuli and pitch shifting, we describe here a short reverse-correlation experiment about the perception of speech intonation. Speech intonation, and notably the temporal pattern of pitch in a given utterance, can be used to convey syntactic or sentence mode information (e.g. whether a sentence is interrogative or declarative), stress (e.g. on what word is the sentence’s focus), emotional expression (e.g. whether a speaker is happy or sad) or attitudinal content (e.g. whether a speaker is confident or doubtful) [37]. For instance, patterns of rising pitch are associated with social traits such as submissiveness, doubt or questioning, and falling pitch with dominance or assertiveness [38–40]. In addition, recent neurophysiological evidence suggests that intonation processing is rooted at early processing stages in the auditory cortex [41]. However, it has remained difficult to attest of the generality of such intonation patterns and of their causality in cognitive mechanisms. Even for information as seemingly simple as the question/statement contrast, which is conventionally associated with the “final Rise” intonation, empirical studies show that, while frequent, this pattern is not as simple nor common as usually believed [42]. For instance, in one analysis of a corpus of 216 questions, the most frequent tone for polar questions (e.g. “Is this a question?”) was a Fall [43]. In addition, in English, interrogative pitch contours do not consistently rise on the final part of the utterance, but rather after the first syllable of the content word [44] (e.g. “Is this a question you’re asking? vs “Is this a question you’re asking?”).
We give here a proof of concept of how to use CLEESE in a reverse-correlation experiment to uncover what exact pitch contour drives participants’ categorization of an utterance as interrogative or declarative. Data come from the first experiment presented in [31].
Methods
Stimuli.
One male speaker recorded a 426ms utterance of the French word “vraiment” (“really”), which can be experienced either as a one-word statement or question. We used CLEESE to artificially manipulate the pitch contour of the recording. First, the original pitch contour (mean pitch = 105Hz) was artificially flattened to constant pitch, using the procedure shown in Fig 3C and 3D. Then, we added/subtracted a constant pitch gain (± 20 cents, equating to ± 1 fifth of a semitone) to create the ‘high-’ or ‘low-pitch’ versions presented in each trial (note that we created these “high” and “low” pitch categories to facilitate participants’ task, but they are not necessary). Finally, we added Gaussian “pitch noise” (i.e. pitch-shifting) to the contour by sampling pitch values at 6 successive time-points, using a normal distribution (SD = 70 cents; clipped at ± 2.2 SD), linearly interpolated between time-points, using the procedure shown in Fig 3A and 3B. The choice of SD in this experiment was set empirically, based on the plausible pitch changes in natural speech (<200 cents between consecutive time points). See S3 Sound for examples of stimuli.
Procedure.
700 pairs of randomly-manipulated voices were presented to each of N = 5 observers (male: 3, M = 22.5yo), all native French speakers with self-reported normal hearing. Participants listened to a pair of two randomly-modulated voices and were asked which of the two versions was most interrogative. Inter-stimulus interval in each trial was 500 ms, and inter-trial interval was 1s.
Apparatus.
The stimuli were mono sound files generated at sampling rate 44.1 kHz in 16-bit resolution by Matlab. They were presented diotically through headphones (Beyerdynamic DT 770 PRO; 80 ohms) at a comfortable sound level.
Analysis.
A first-order temporal kernel [45] (i.e., a 7-points vector) was computed for each participant, as the mean pitch contour of the voices classified as interrogative minus the mean pitch contour of the voices classified as non-interrogative. Kernels were then normalized by dividing them by the absolute sum of their values and then averaged over all participants for visualization. A one-way repeated-measures ANOVA was conducted on the temporal kernels to test for an effect of segment on pitch shift, and posthocs were computed using Bonferroni-corrected Tukey tests. All experimental data and analysis code for Case Study 1 is provided in supporting information, as S1 Data and S1 and S2 Codes.
Results
Observers’ responses revealed mental representations of interrogative prosody showing a consistent increase of pitch at the end of the second syllable of the stimulus word (Fig 4-left), as reflected by a main effect of segment index: F(6, 24) = 35.84, p = 7.8e-11. Pitch shift at segment 5 (355ms) was significantly different from all other segment locations (all ps <0.001). The pattern was remarkably consistent among participants, although all participants were tested on separate set of random stimuli (Fig 4-right).
Left: Average first-order kernel derived by reverse-correlation on N = 5 participants, showing an increase of pitch at the end of the second syllable. Right: individual kernel from each of the participants.
Case-study (2): Musical rhythm
To illustrate another use of CLEESE, this time with musical stimuli and the time-stretching functionality, we describe here a second reverse-correlation experiment about the perception of musical rhythm and expressive timing.
The study of how participants perceive or accurately reproduce the rhythm of musical phrases has informed such domain-general questions as how humans represent sequences of events [46], internally measure speed and tempo [47] and entrain to low- and high-frequency event trains [48]. For instance, it is often observed that musicians tend to lengthen notes at the ends of musical phrases [49] and that even non-musicians anticipate such changes when they perceive music [50]. However, the cognitive structures that govern a participant’s representation of the rhythm of a given musical passage are difficult to uncover with experimental methods. In [50], it was accessed indirectly by measuring the ability to detect timing errors inserted at different positions in a phrase; in [51], participants were asked to manually advance through a sequence of musical chords with a key press, and the time dwelt on each successive chord was used to quantify how fast they internally represented the corresponding musical time. Here, we give a proof of concept of how to use CLEESE in a reverse-correlation experiment to uncover what temporal contour drives participants’ judgement of a rhythmically competent/accurate rendition of the well-known song Happy birthday.
Methods
Stimuli.
One female singer recorded a a capella rendering of the first phrase of the French folk song “Joyeux anniversaire” (♪. ♩♩♩
; translation of English song “Happy birthday to you” [52]). We used CLEESE to artificially manipulate the timing of the recording by stretching it between different breakpoints. First, we manually identified the time onset of each sung note in the phrase, and use these positions as breakpoints. Second, the original temporal contour was artificially flattened (i.e. all notes were stretched/compressed to have identical duration ♩ ♩ ♩ ♩ ♩ ♩), while preserving the original pitch of each note. The duration of this final stimulus was 3203ms. Then, we added Gaussian “temporal noise” (i.e. time-stretching) to each note by sampling stretch values at 6 successive time-points, using a normal distribution centered at 1 (SD = 0.4; clipped at ± 1.6 SD), using a square BPF with a transition time of 0.1 s. The resulting stimuli were therefore sung variants of the same melody, with the original pitch class, but random rhythm (see S4 Sound).
Procedure.
Pairs of these randomly-manipulated sung phrases were presented to N = 12 observers (male: 6, M = 22yo), all native French speakers with self-reported normal hearing. Five participants had previous musical training (more than 12 years of instrumental practice) and were therefore considered as musicians, the other seven participants had no musical training and were considered as non-musicians. Participants listened to a pair of two randomly-modulated phrases and were asked which of the two versions was best sung/performed. Inter-stimulus interval in each trial was 500 ms, and inter-trial interval was 1s. Each participant was presented with a total of 313 trials. There were 280 different trials and the last block of 33 trials was repeated twice (in the same order) to estimate the percentage of agreement. After the test, all participants complete the rhythm, melody, rythm-melody subtests of the Profile of Music Perception Skills (PROMS, [53]), in order to quantify their melodic and rhythmic perceptual abilities.
Analysis.
A first-order temporal kernel [45] (i.e., a 6-points vector) was computed for each participant, as the mean stretch contour of the phrases classified as ‘good performances’ minus the mean stretch contour of the phrases classified as ‘bad performances’ (note that because stretch factors are ratio, the kernel was in fact computed in the log stretch domain, as mean log t+ − mean log t−, where t+ and t− are the contours of selected and non-selected trials, resp.). Kernels were then normalized by dividing them by the absolute sum of their values and then averaged over all participants. A one-way repeated-measures ANOVA was conducted on the temporal kernels to test for an effect of segment on time-stretch, and posthocs were computed using Bonferroni-corrected Tukey tests. In addition, the amount of internal noise for each subject was computed from the double-pass percentage of agreement, using a signal detection theory model including response bias and late additive noise [54, 55]. All experimental data and analysis code for Case Study 2 is provided in supporting information, as S1 Data and S1 and S2 Codes.
Results
Observers’ responses revealed mental representations of note durations that significantly evolve through time (Fig 5-left) with a main effect of time index: F(5, 40) = 30.7, p = 1e-12. The ideal theoretical timing contour (0.75-0.25-1-1-1-2), as given by the score of this musical phrase, was converted in time-stretch units by taking the log of these values and was superimposed on Fig 5. Several deviations from this theoretical contour are worth noting. First, both musicians and non-musicians had shortened representations for the first note of the melody (♪. →
) and, to a lesser extent, the second note. This is consistent with previous findings showing that expressive timing typically involving a shortening of the short notes, and a lengthening of the long notes of a melody [49]. Second, both musicians and non-musicians had similarly shortened representations for the duration of the last note (♩ ♩ ♩
→ ♩ ♩ ♩ ♪), a pattern is at odds with the note-final lengthening reported for non-musicians in a self-paced listening paradigm [51], but consistent with the lengthening of the last-but-one inter-onset-interval found in professional pianists [49, 56]. Finally, the analysis revealed a significant interaction of time index and participant musicianship: F(5, 40) = 2.5, p = .045. The mental representations of a well-executed song for the non-musician participants had longer durations on the third note (“happy BIRTH-day to you”) compared with musicians participants. Needless to say, these differences between musicians and non-musicians are only provided for illustrative purposes, because of the small-powered nature of this case-study, and their experimental confirmation and interpretation remain the object of future work.
Left: Average first-order kernel derived by reverse-correlation on N = 12 participants, superimposed with the theoretical pattern (dotted line), showing how the internally represented pattern departed from the theoretical one. Right: log-values of internal noise estimated from the double-pass technique are negatively correlated with the participant’s degree of musical skills (average score obtained for the 3 sub-tests of PROMS); r = -0.65, p = 0.02.
One should note that it is complicated to quantitatively compare the time-stretch contours obtained with reverse-correlation with an ideal theoretical pattern. First, time-stretch factors derived by reverse-correlation have arbitrary scale, and the ideal pattern is therefore be represented at an arbitrary position on the y-axis, and rescaled to have the same (max-min) stretch range as the one obtained experimentally. Second, because participants’ task is to infer about the best overall rhythm and not individual note duration, the time-stretch values at individual time points are not perceptually independent (e.g., if the first two notes of a stimulus are long, participants may infer a slower beat and expect even longer values for the following notes). For these reasons, the ideal rhythmic pattern superimposed on these measured patterns should simply be taken as an illustration of how note durations theoretically evolve from one note (i) to the next (i+1). Further theoretical work will be needed to best analyze and interpret duration kernels derived from such reverse-correlation experiments.
In addition, internal noise values computed from the repeated block of this experiment indicate that subjects did not behave at random but rather relied on a somewhat precise mental representation of the ideal temporal contour (mean internal noise of 1.1 (SD = 1.1) in units of external noise; comparable with the average value of 1.3 obtained in typical low-level sensory psychophysical detection or discrimination tasks, see [55]). In an exploratory manner, we asked whether these internal noise values would correlate with the participant’s degree of musical skills (average score obtained for the 3 sub-tests of PROMS). We found a significant negative correlation (r = -0.65, p = 0.02), suggesting that low musical skills (regardless of musicianship) are associated with a more variable mental/memory representation of the temporal contour of this melody (Fig 5-right). Because this correlation may also be driven by the amount of attention that participants had both in the PROMS and in the reverse-correlation tasks (which may be similar), further work is required to determine the exact nature of this observed relationship.
Discussion
By providing the ability to manipulate speech and musical dimensions such as pitch, duration and amplitude in a parametric, independent manner in the common environment of the Python programming language, the open-source toolbox CLEESE brings the power of data-driven/reverse-correlation methods to the vast domain of speech and music cognition. In two illustrative case-studies, we have used CLEESE to infer listeners’ mental representations of interrogative intonation (rising pitch at the end of the utterance) and of the rhythmic structure of a well-known musical melody, and shown that the methodology had potential to uncover potential individual differences linked, e.g., to participant’s training or perceptual abilities. As such, the toolbox and the associated methodology open avenues of research in communicative behavior and social cognition. As a first application of CLEESE, we have recently used a reverse-correlation paradigm to uncover what mental representations of pitch profiles underlie judgements of speaker dominance and trustworthiness in short utterances like the word ‘hello’ [31]. The technique allowed to establish that both constructs corresponded to robust and distinguishing pitch trajectories, which remained remarkably stable whether males or female listeners judged male or female speakers. Other potential questions include, in speech, studies of expressive intonation along all characteristics of pitch, speed and amplitude, judgements of emotions (e.g. being happy, angry or sad) or attitudes (e.g. being critical, impressed or ironic); in music, studies of melodic and rythmic representations in naive and expert listeners, and how these may differ with training or exposure. Beyond speech and music, CLEESE can also be used to transform an study non-verbal vocalizations, such a infant cries or animal calls.
By measuring how any given individual’s or population’s mental representations may differ from the generic code, data-driven paradigms have been especially important in studying individual or cultural differences in face [2, 57] or lexical processing [23]. By providing a similar paradigm to map mental representations in the vast domain of speech prosody, the present technique opens avenues to explore e.g. dysprosody and social-cognitive deficits in autism-spectrum disorder [58], schizophrenia [59] or congenital amusia [60], as well as cultural differences in social and affective prosody [61].
Because CLEESE allows to create random variations among the different dimensions of both speech and musical stimuli (pitch, time, level), it also opens possibilities to measure the amount of internal noise (e.g. using a double-pass technique as in the case-study 2 here) associated to the processing of these dimensions in many various high-level cognitive tasks. This is particularly interesting because it provides a quantitative way to (1) demonstrate that participants are not doing the task at random (which is always an issue in this type of high-level task where there is no good/bad answers that would lead to an associated d-prime value) and (2) investigate which perceptual dimensions are cognitively processed with what amount of noise.
The current implementation of CLEESE, and its application to data-driven experiments in the auditory modality, leaves several methodological questions open. First, one important choice to make when generating stimuli is the SD of the Gaussian distributions used to draw parameters’ values (pitch in case study 1; time-stretch in case study 2). Our rationale in this work has been to choose SDs that correspond to the typical variation observed for each parameter in natural speech; e.g. <200 cents (SD = 70cents cut at 2.2SD) for pitch contours in the first case study. We find this empirical approach appropriate in practice, since it allows creating stimuli that sound natural. However, it would be interesting to examine the optimality of this choice in future studies, for example by testing different values of SDs or using an adaptive staircase procedure to select the one that is best suited to uncover the mental representations. It is important to note that, theoretically, first-order kernels do not depend on the choice of SD, as filters are scaled in units of external noise variance, i.e. SD [4]. However, it is reasonable to expect that using low values of SD can lead to poor kernel resolution (because many trials would not be audible), and high values can create undesired sound artefacts or inconsistencies (e.g. pitch variations that are too high or fast, or would not occur with a natural voice production system). Therefore, basing the value of SD on the range of variation measured in natural voices, or on pilot/preliminary experiments seems the most appropriate choice.
Second, in the current form of the software, the temporal dynamics of the noise/perturbations is purely random, and changes of pitch/time or amplitude are independent across segments (a classical assumption of the reverse-correlation technique [4]). This assumption may create prosodic patterns that are not necessary realizable by the human voice and thus bias the participant responses for those trials. In vision, recent studies have manipulated facial action units with a restricted family of temporal profiles parametrized in acceleration, amplitude and relative length [25]. In a similar manner, future versions of CLEESE could constrain prosodic patterns to correspond more closely to the dynamics of the human voice or to the underlying production process, e.g. not aligned with arbitrary segment locations but with the underlying phonemic (or musical) structure. A third important question when running reverse-correlation experiments concerns how many trials are necessary to obtain stable kernel. Theoretically, the optimal number of trials depends not only on the number of points that are manipulated (i.e. the size of the BPF, e.g. 7 time points in case study 1), but also on the number of points that actually drive participants’ judgments (e.g. in case study 1, only 2 or 3 points, the weights given to other time points being virtually null), the complexity of the judgement (and how far from linear it is), the suitability of the dimension considered to probe the mental representation (here, pitch), as well as the amount of internal noise. In practice, it is therefore almost impossible to predict the required number of trials without having collected preliminary data. To illustrate the “speed” at which our measure of kernel converges towards the final measure in data from case-study 1, we computed the correlation between the kernel derived using the n first trials and the kernel derived using all 700 trials, for increasing values of n (Fig 6). For most subjects, the correlation reaches already.8 when considering only the first 100 trials; but for some subjects (e.g. yellow line), it takes 300 trials to get the same correlation. Overall, it is clear that the choice of having 700 trials for that task was overestimated, and that we could have reached the same precision with fewer trials. Other tasks, however, may require several thousands of trials for each participant to reach the same correlation. A good practice when using this method therefore seems to run a few participants before the main experiment, and determine how much trials are required to reach stable estimates.
Correlation between the kernel derived using the n first trials of case-study 1, and the kernel derived using all trials (n = 700); n varying by steps of 20 trials (individual curves: Thin colored lines; averaged ± sem: Thick black line with shaded error bars). These curves reflect the “speed” at which our measure converges toward the final kernel estimate (insets show temporary kernels at stages n = 100, n = 300 and n = 700, averaged across subjects in black line, and for the subject with the slowest convergence in yellow).
On a technical level, another possible direction for improvement is the addition of other high-level manipulation dimensions. As an example, spectral envelope manipulation (optionally formant-driven) allows powerful transformations related to timbre, speaker identity and even gender which all could be manipulated by CLEESE [62]. At an even higher abstraction level, research could consider more semantically-related features (such as the mid-level audio descriptors typically used in machine learning methods) or even feature-learning approaches that would automatically derive the relevant dimensions prior to randomization [63]. Adding these new dimensions will likely require a trade-off between their effectiveness for stimuli randomization and their suitability for physical interpretation. Finally, improvements of the current phase vocoder implementation in CLEESE may include additional modules such as envelope or transient preservation to further improve the realism of the transformations.
Supporting information
S1 Sound. Audio example of pitch shifting manipulation with CLEESE, on French sentence “je suis en route pour la réunion” (I’m on my way to the meeting).
https://doi.org/10.1371/journal.pone.0205943.s001
(MP3)
S2 Sound. Audio example of time stretching manipulation with CLEESE, on French sentence “je suis en route pour la réunion” (I’m on my way to the meeting).
https://doi.org/10.1371/journal.pone.0205943.s002
(MP3)
S3 Sound. Example of trial for case-study 1, containing a pair of random pitch shifting of the word ‘vraiment’ (really?).
https://doi.org/10.1371/journal.pone.0205943.s003
(MP3)
S4 Sound. Example of trial for case-study 2, containing a pair of random time stretching of the song ‘joyeux anniversaire’ (happy birthday to you).
https://doi.org/10.1371/journal.pone.0205943.s004
(MP3)
S1 Data. Reverse-correlation data for case-studies 1 and 2, in csv format.
See provided Python notebook for format and analysis.
https://doi.org/10.1371/journal.pone.0205943.s005
(ZIP)
S1 Code. Python notebook with analysis code for case-studies 1 and 2, recreating the articles’ figures and statistical analyses.
Use with S1_Data.zip.
https://doi.org/10.1371/journal.pone.0205943.s006
(IPYNB)
Acknowledgments
The authors thank Etienne Thoret, Leo Varnet, Vincent Isnard for useful discussions on the audio signal processing aspects of this work, as well as Pascal Belin, Frederic Gosselin, Philippe Schyns and Rachael Jack for discussions on data analysis. We thank Louise Vasa for help with data collection. All data collected at the Sorbonne-Université INSEAD Center for Behavioural Sciences. This study was supported by ERC Grant 335536 CREAM to JJA.
References
- 1. Ekman P, Sorenson ER, Friesen WV. Pan-cultural elements in facial displays of emotion. Science. 1969;164(3875):86–88. pmid:5773719
- 2. Jack RE, Garrod OG, Yu H, Caldara R, Schyns PG. Facial expressions of emotion are not culturally universal. Proceedings of the National Academy of Sciences. 2012;109:7241–7244.
- 3. Juslin PN, Laukka P. Communication of emotions in vocal expression and music performance: Different channels, same code? Psychological bulletin. 2003;129(5):770. pmid:12956543
- 4. Murray RF. Classification images: A review. Journal of Vision. 2011;11(5):2–2. pmid:21536726
- 5. Adolphs R, Nummenmaa L, Todorov A, Haxby JV. Data-driven approaches in the investigation of social perception. Phil Trans R Soc B. 2015;371(1693):20150367.
- 6. De Boer E, Kuyper P. Triggered correlation. IEEE Transactions on Biomedical Engineering. 1968;(3):169–179. pmid:5667803
- 7. Marmarelis PZ, Naka KI. White-noise analysis of a neuron chain: an application of the Wiener theory. Science. 1972;175(4027):1276–1278. pmid:5061252
- 8. Eggermont JJ, Johannesma PIM, Aertsen AMHJ. Reverse-correlation methods in auditory research. Quarterly reviews of biophysics. 1983;16(03):341–414. pmid:6366861
- 9. Ringach D, Shapley R. Reverse correlation in neurophysiology. Cognitive Science. 2004;28(2):147–166.
- 10. Ahumada A, Lovell J. Stimulus features in signal detection. Journal of the Acoustical Society of America. 1971;49(6B):1751–1756.
- 11. Ponsot E, Susini P, Saint Pierre G, Meunier S. Temporal loudness weights for sounds with increasing and decreasing intensity profiles. The Journal of the Acoustical Society of America. 2013;134(4):EL321–EL326. pmid:24116537
- 12. Mangini MC, Biederman I. Making the ineffable explicit: Estimating the information employed for face classifications. Cognitive Science. 2004;28(2):209–226.
- 13. Gosselin F, Schyns PG. Bubbles: a technique to reveal the use of information in recognition tasks. Vision research. 2001;41(17):2261–2271. pmid:11448718
- 14. Dotsch R, Todorov A. Reverse correlating social face perception. Social Psychological and Personality Science. 2012;3(5):562–571.
- 15. Adolphs R, Gosselin F, Buchanan TW, Tranel D, Schyns P, Damasio AR. A mechanism for impaired fear recognition after amygdala damage. Nature. 2005;433(7021):68–72. pmid:15635411
- 16. Venezia JH, Hickok G, Richards VM. Auditory “bubbles”: Efficient classification of the spectrotemporal modulations essential for speech intelligibility. The Journal of the Acoustical Society of America. 2016;140(2):1072–1088. pmid:27586738
- 17. Mandel MI, Yoho SE, Healy EW. Measuring time-frequency importance functions of speech with bubble noise. Journal of the Acoustical Society of America. 2016;140:2542–2553. pmid:27794278
- 18. Varnet L, Wang T, Peter C, Meunier F, Hoen M. How musical expertise shapes speech perception: evidence from auditory classification images. Scientific reports. 2015;5. pmid:26399909
- 19. Brimijoin WO, Akeroyd MA, Tilbury E, Porr B. The internal representation of vowel spectra investigated using behavioral response-triggered averaging. The Journal of the Acoustical Society of America. 2013;133(2):EL118–EL122. pmid:23363191
- 20.
Isnard V, Suied C, Lemaitre G. Auditory bubbles reveal sparse time-frequency cues subserving identification of musical voices and instruments. In: Meeting of the Acoustical Society of America. vol. 140; 2016. p. 3267.
- 21. Thoret E, Depalle P, McAdams S. Perceptually Salient Regions of the Modulation Power Spectrum for Musical Instrument Identification. Frontiers in psychology. 2017;8:587. pmid:28450846
- 22. Gosselin F, Schyns PG. Superstitious perceptions reveal properties of internal representations. Psychological Science. 2003;14(5):505–509. pmid:12930484
- 23. Varnet L, Wang T, Peter C, Meunier F, Hoen M. How musical expertise shapes speech perception: evidence from auditory classification images. Scientific reports. 2015;5. pmid:26399909
- 24. Roesch EB, Tamarit L, Reveret L, Grandjean D, Sander D, Scherer KR. FACSGen: A tool to synthesize emotional facial expressions through systematic manipulation of facial action units. Journal of Nonverbal Behavior. 2011;35(1):1–16.
- 25. Yu H, Garrod OG, Schyns PG. Perception-driven facial expression synthesis. Computers & Graphics. 2012;36(3):152–162.
- 26. Rychlowska M, Jack RE, Garrod OG, Schyns PG, Martin JD, Niedenthal PM. Functional smiles: Tools for love, sympathy, and war. Psychological science. 2017;28(9):1259–1270. pmid:28741981
- 27. Jack RE, Garrod OG, Schyns PG. Dynamic facial expressions of emotion transmit an evolving hierarchy of signals over time. Current biology. 2014;24(2):187–192. pmid:24388852
- 28. Stivers T. An overview of the question—response system in American English conversation. Journal of Pragmatics. 2010;42(10):2772–2781.
- 29. Saffran JR, Newport LE, Aslin RN. Word segmentation: The role of distributional cues. Journal of Memory and Language. 1996;35:606–621.
- 30. Kochanski G, Grabe E, Coleman J, Rosner B. Loudness predicts prominence: Fundamental frequency lends little. The Journal of the Acoustical Society of America. 2005;118(2):1038–1054. pmid:16158659
- 31.
Ponsot E, Burred JJ, Belin P, Aucouturier JJ. Cracking the social code of speech prosody using reverse correlation. Proceedings of the National Academy of Sciences. 2018; p. 201716090.
- 32. Ponsot E, Arias P, Aucouturier JJ. Uncovering mental representations of smiled speech using reverse correlation. The Journal of the Acoustical Society of America. 2018;143(1):EL19–EL24. pmid:29390775
- 33. Portnoff M. Implementation of the digital phase vocoder using the fast Fourier transform. IEEE Trans Acoustics, Speech and Signal Processing. 1976;24(3):243–248.
- 34. Dolson M. The phase vocoder: A tutorial. Computer Music Journal. 1986;10:14–27.
- 35. Laroche J, Dolson M. Improved phase vocoder time-scale modification of audio. IEEE Trans Speech and Audio Processing. 1999;7(3):323–332.
- 36. Liuni M, Roebel A. Phase vocoder and beyond. Musica, Tecnologia. 2013;7:73–120.
- 37.
Gussenhoven C. The phonology of tone and intonation. Cambridge University Press; 2004.
- 38. Ohala JJ. An ethological perspective on common cross-language utilization of F0 of voice. Phonetica. 1984;41:1–16. pmid:6204347
- 39. Mitchell RL, Ross ED. Attitudinal prosody: What we know and directions for future study. Neuroscience & Biobehavioral Reviews. 2013;37:471–479.
- 40. Jiang X, Pell MD. The sound of confidence and doubt. Speech Communication. 2017;88:106–126.
- 41. Tang C, Hamilton L, Chang E. Intonational speech prosody encoding in the human auditory cortex. Science. 2017;357(6353):797–801. pmid:28839071
- 42. Stivers T. An overview of the question—response system in American English conversation. Journal of Pragmatics. 2010;42(10):2772–2781.
- 43. Geluykens R. On the myth of rising intonation in polar questions. Journal of Pragmatics. 1988;12(4):467–485.
- 44.
Liu F, Xu Y. Question intonation as affected by word stress and focus in English. In: Proceedings of the 16th international congress of phonetic sciences. International Congress of Phonetic Sciences Saarbrücken; 2007. p. 1189–1192.
- 45. Ahumada A Jr, Lovell J. Stimulus features in signal detection. The Journal of the Acoustical Society of America. 1971;49(6B):1751–1756.
- 46. Povel DJ, Essens P. Perception of temporal patterns. Music Perception: An Interdisciplinary Journal. 1985;2(4):411–440.
- 47. Levitin DJ, Cook PR. Memory for musical tempo: Additional evidence that auditory memory is absolute. Perception & Psychophysics. 1996;58(6):927–935.
- 48. Doelling KB, Poeppel D. Cortical entrainment to music and its modulation by expertise. Proceedings of the National Academy of Sciences. 2015;112(45):E6233–E6242.
- 49. Palmer C. Mapping musical thought to musical performance. Journal of experimental psychology: human perception and performance. 1989;15(2):331. pmid:2525602
- 50. Repp BH. Probing the cognitive representation of musical time: Structural constraints on the perception of timing perturbations. Cognition. 1992;44(3):241–281. pmid:1424494
- 51. Kragness HE, Trainor LJ. Listeners lengthen phrase boundaries in self-paced music. Journal of Experimental Psychology: Human Perception and Performance. 2016;42(10):1676. pmid:27379872
- 52. Brauneis R. Copyright and the World’s Most Popular Song. J Copyright Soc’y USA. 2008;56:335.
- 53. Law LN, Zentner M. Assessing musical abilities objectively: Construction and validation of the Profile of Music Perception Skills. PloS one. 2012;7(12):e52508. pmid:23285071
- 54. Burgess A, Colborne B. Visual signal detection. IV. Observer inconsistency. JOSA A. 1988;5(4):617–627.
- 55. Neri P. How inherently noisy is human sensory processing? Psychonomic Bulletin & Review. 2010;17(6):802–808.
- 56. Penel A, Drake C. Timing variations in music performance: Musical communication, perceptual compensation, and/or motor control? Perception & Psychophysics. 2004;66(4):545–562.
- 57. Adolphs R, Gosselin F, Buchanan TW, Tranel D, Schyns PG, Damasio AR. A mechanism for impaired fear recognition after amygdala damage. Nature. 2005;433:68–72. pmid:15635411
- 58. Jiang J, Liu X, Wan X, Jiang C. Perception of Melodic Contour and Intonation in Autism Spectrum Disorder: Evidence From Mandarin Speakers. Journal of autism and developmental disorders. 2015;45:2067–2075. pmid:25636678
- 59. Pinheiro AP, Del Re E, Mezin J, Nestor PG, Rauber A, McCarley RW, et al. Sensory-based and higher-order operations contribute to abnormal emotional prosody processing in schizophrenia: an electrophysiological investigation. Psychological Medicine. 2013;43:603–618. pmid:22781212
- 60. Liu F, Patel AD, Fourcin A, Stewart L. Intonation processing in congenital amusia: discrimination, identification and imitation. Brain. 2010;133:1682–1693. pmid:20418275
- 61. Sauter DA, Eisner F, Ekman P, Scott SK. Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations. Proceedings of the National Academy of Sciences. 2010;107(6):2408–2412.
- 62.
Arias P, Soladie C, Bouafif O, Robel A, Seguier R, Aucouturier JJ. Realistic transformation of facial and vocal smiles in real-time audiovisual streams. IEEE Transactions on Affective Computing. 2018.
- 63.
Larsen ABL, Sønderby SK, Larochelle H, Winther O. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:151209300. 2015;.