Auditory Sketches: Very Sparse Representations of Sounds Are Still Recognizable

Learning metrics on spectrotemporal modulations reveals the perception of musical instrument timbre

Humans excel at using sounds to make judgements about their immediate environment. In particular, timbre is an auditory attribute that conveys crucial information about the identity of a sound source, especially for music. While timbre has been primarily considered to occupy a multidimensional space, unravelling the acoustic correlates of timbre remains a challenge. Here we re-analyse 17 datasets from published studies between 1977 and 2016 and observe that original results are only partially replicable. We use a data-driven computational account to reveal the acoustic correlates of timbre. Human dissimilarity ratings are simulated with metrics learned on acoustic spectrotemporal modulation models inspired by cortical processing. We observe that timbre has both generic and experiment-specific acoustic correlates. These findings provide a broad overview of former studies on musical timbre and identify its relevant acoustic substrates according to biologically inspired models.

Sculpting speech from noise, music, and other sources

Intelligible speech can be generated by passing a signal through a time-frequency mask that selects which information to retain, even when the signal is speech-shaped noise, suggesting an important role for the mask pattern itself. The current study examined the relationship between the signal and the mask by varying the availability of target speech cues in the signal while holding the mask constant. Keyword identification rates in everyday sentences varied from near-ceiling to near-floor levels as the signal was varied, indicating that the interaction between the signal and mask, rather than the mask alone, determines intelligibility.

The time course of auditory recognition measured with rapid sequences of short natural sounds

Human listeners are able to recognize accurately an impressive range of complex sounds, such as musical instruments or voices. The underlying mechanisms are still poorly understood. Here, we aimed to characterize the processing time needed to recognize a natural sound. To do so, by analogy with the “rapid visual sequential presentation paradigm”, we embedded short target sounds within rapid sequences of distractor sounds. The core hypothesis is that any correct report of the target implies that sufficient processing for recognition had been completed before the time of occurrence of the subsequent distractor sound. We conducted four behavioral experiments using short natural sounds (voices and instruments) as targets or distractors. We report the effects on performance, as measured by the fastest presentation rate for recognition, of sound duration, number of sounds in a sequence, the relative pitch between target and distractors and target position in the sequence. Results showed a very rapid auditory recognition of natural sounds in all cases. Targets could be recognized at rates up to 30 sounds per second. In addition, the best performance was observed for voices in sequences of instruments. These results give new insights about the remarkable efficiency of timbre processing in humans, using an original behavioral paradigm to provide strong constraints on future neural models of sound recognition.

Perceptually Salient Regions of the Modulation Power Spectrum for Musical Instrument Identification

The ability of a listener to recognize sound sources, and in particular musical instruments from the sounds they produce, raises the question of determining the acoustical information used to achieve such a task. It is now well known that the shapes of the temporal and spectral envelopes are crucial to the recognition of a musical instrument. More recently, Modulation Power Spectra (MPS) have been shown to be a representation that potentially explains the perception of musical instrument sounds. Nevertheless, the question of which specific regions of this representation characterize a musical instrument is still open. An identification task was applied to two subsets of musical instruments: tuba, trombone, cello, saxophone, and clarinet on the one hand, and marimba, vibraphone, guitar, harp, and viola pizzicato on the other. The sounds were processed with filtered spectrotemporal modulations with 2D Gaussian windows. The most relevant regions of this representation for instrument identification were determined for each instrument and reveal the regions essential for their identification. The method used here is based on a “molecular approach,” the so-called bubbles method. Globally, the instruments were correctly identified and the lower values of spectrotemporal modulations are the most important regions of the MPS for recognizing instruments. Interestingly, instruments that were confused with each other led to non-overlapping regions and were confused when they were filtered in the most salient region of the other instrument. These results suggest that musical instrument timbres are characterized by specific spectrotemporal modulations, information which could contribute to music information retrieval tasks such as automatic source recognition.

Vocal Imitations of Non-Vocal Sounds

Imitative behaviors are widespread in humans, in particular whenever two persons communicate and interact. Several tokens of spoken languages (onomatopoeias, ideophones, and phonesthemes) also display different degrees of iconicity between the sound of a word and what it refers to. Thus, it probably comes at no surprise that human speakers use a lot of imitative vocalizations and gestures when they communicate about sounds, as sounds are notably difficult to describe. What is more surprising is that vocal imitations of non-vocal everyday sounds (e.g. the sound of a car passing by) are in practice very effective: listeners identify sounds better with vocal imitations than with verbal descriptions, despite the fact that vocal imitations are inaccurate reproductions of a sound created by a particular mechanical system (e.g. a car driving by) through a different system (the voice apparatus). The present study investigated the semantic representations evoked by vocal imitations of sounds by experimentally quantifying how well listeners could match sounds to category labels. The experiment used three different types of sounds: recordings of easily identifiable sounds (sounds of human actions and manufactured products), human vocal imitations, and computational “auditory sketches” (created by algorithmic computations). The results show that performance with the best vocal imitations was similar to the best auditory sketches for most categories of sounds, and even to the referent sounds themselves in some cases. More detailed analyses showed that the acoustic distance between a vocal imitation and a referent sound is not sufficient to account for such performance. Analyses suggested that instead of trying to reproduce the referent sound as accurately as vocally possible, vocal imitations focus on a few important features, which depend on each particular sound category. These results offer perspectives for understanding how human listeners store and access long-term sound representations, and sets the stage for the development of human-computer interfaces based on vocalizations.

Perceptually salient spectrotemporal modulations for recognition of sustained musical instruments

Modulation Power Spectra include dimensions of spectral and temporal modulation that contribute significantly to the perception of musical instrument timbres. Nevertheless, it remains unknown whether each instrument's identity is characterized by specific regions in this representation. A recognition task was applied to tuba, trombone, cello, saxophone, and clarinet sounds resynthesized with filtered spectrotemporal modulations. The most relevant parts of this representation for instrument identification were determined for each instrument. In addition, instruments that were confused with each other led to non-overlapping spectrotemporal modulation regions, suggesting that musical instrument timbres are characterized by specific spectrotemporal modulations.