Vocal imitation of percussion sounds: On the perceptual similarity between imitations and imitated sounds

Recent studies have demonstrated the effectiveness of the voice for communicating sonic ideas, and the accuracy with which it can be used to imitate acoustic instruments, synthesised sounds and environmental sounds. However, there has been little research on vocal imitation of percussion sounds, particularly concerning the perceptual similarity between imitations and the sounds being imitated. In the present study we address this by investigating how accurately musicians can vocally imitate percussion sounds, in terms of whether listeners consider the imitations 'more similar' to the imitated sounds than to other same-category sounds. In a vocal production task, 14 musicians imitated 30 drum sounds from five categories (cymbals, hats, kicks, snares, toms). Listeners were then asked to rate the similarity between the imitations and same-category drum sounds via web based listening test. We found that imitated sounds received the highest similarity ratings for 16 of the 30 sounds. The similarity between a given drum sound and its imitation was generally rated higher than for imitations of another same-category sound, however for some drum categories (snares and toms) certain sounds were consistently considered most similar to the imitations, irrespective of the sound being imitated. Finally, we apply an existing auditory image based measure for perceptual similarity between same-category drum sounds, to model the similarity ratings using linear mixed effect regression. The results indicate that this measure is a good predictor of perceptual similarity between imitations and imitated sounds, when compared to acoustic features containing only temporal or spectral features

Vocal imitation for query by vocalisation

PhD ThesisThe human voice presents a rich and powerful medium for expressing sonic ideas such as musical sounds. This capability extends beyond the sounds used in speech, evidenced for example in the art form of beatboxing, and recent studies highlighting the utility of vocal imitation for communicating sonic concepts. Meanwhile, the advance of digital audio has resulted in huge libraries of sounds at the disposal of music producers and sound designers. This presents a compelling search problem: with larger search spaces, the task of navigating sound libraries has become increasingly difficult. The versatility and expressive nature of the voice provides a seemingly ideal medium for querying sound libraries, raising the question of how well humans are able to vocally imitate musical sounds, and how we might use the voice as a tool for search. In this thesis we address these questions by investigating the ability of musicians to vocalise synthesised and percussive sounds, and evaluate the suitability of different audio features for predicting the perceptual similarity between vocal imitations and imitated sounds. In the first experiment, musicians were tasked with imitating synthesised sounds with one or two time–varying feature envelopes applied. The results show that participants were able to imitate pitch, loudness, and spectral centroid features accurately, and that imitation accuracy was generally preserved when the imitated stimuli combined two, non-necessarily congruent features. This demonstrates the viability of using the voice as a natural means of expressing time series of two features simultaneously. The second experiment consisted of two parts. In a vocal production task, musicians were asked to imitate drum sounds. Listeners were then asked to rate the similarity between the imitations and sounds from the same category (e.g. kick, snare etc.). The results show that drum sounds received the highest similarity ratings when rated against their imitations (as opposed to imitations of another sound), and overall more than half the imitated sounds were correctly identified with above chance accuracy from the imitations, although this varied considerably between drum categories. The findings from the vocal imitation experiments highlight the capacity of musicians to vocally imitate musical sounds, and some limitations of non– verbal vocal expression. Finally, we investigated the performance of different audio features as predictors of perceptual similarity between the imitations and imitated sounds from the second experiment. We show that features learned using convolutional auto–encoders outperform a number of popular heuristic features for this task, and that preservation of temporal information is more important than spectral resolution for differentiating between the vocal imitations and same–category drum sounds

Data-Driven Query by Vocal Percussion

The imitation of percussive sounds via the human voice is a natural and effective tool for communicating rhythmic ideas on the fly. Query by Vocal Percussion (QVP) is a subfield in Music Information Retrieval (MIR) that explores techniques to query percussive sounds using vocal imitations as input, usually plosive consonant sounds. In this way, fully automated QVP systems can help artists prototype drum patterns in a comfortable and quick way, smoothing the creative workflow as a result. This project explores the potential usefulness of recent data-driven neural network models in two of the most important tasks in QVP. Algorithms relative to Vocal Percussion Transcription (VPT) detect and classify vocal percussion sound events in a beatbox-like performance so to trigger individual drum samples. Algorithms relative to Drum Sample Retrieval by Vocalisation (DSRV) use input vocal imitations to pick appropriate drum samples from a sound library via timbral similarity. Our experiments with several kinds of data-driven deep neural networks suggest that these achieve better results in both VPT and DSRV compared to traditional data-informed approaches based on heuristic audio features. We also find that these networks, when paired with strong regularisation techniques, can still outperform data-informed approaches when data is scarce. Finally, we gather several insights relative to people’s approach to vocal percussion and how user-based algorithms are essential to better model individual differences in vocalisation styles

Vocal imitation for query by vocalisation

PhDThe human voice presents a rich and powerful medium for expressing sonic ideas such as musical sounds. This capability extends beyond the sounds used in speech, evidenced for example in the art form of beatboxing, and recent studies highlighting the utility of vocal imitation for communicating sonic concepts. Meanwhile, the advance of digital audio has resulted in huge libraries of sounds at the disposal of music producers and sound designers. This presents a compelling search problem: with larger search spaces, the task of navigating sound libraries has become increasingly difficult. The versatility and expressive nature of the voice provides a seemingly ideal medium for querying sound libraries, raising the question of how well humans are able to vocally imitate musical sounds, and how we might use the voice as a tool for search. In this thesis we address these questions by investigating the ability of musicians to vocalise synthesised and percussive sounds, and evaluate the suitability of different audio features for predicting the perceptual similarity between vocal imitations and imitated sounds. In the fi rst experiment, musicians were tasked with imitating synthesised sounds with one or two time{varying feature envelopes applied. The results show that participants were able to imitate pitch, loudness, and spectral centroid features accurately, and that imitation accuracy was generally preserved when the imitated stimuli combined two, non-necessarily congruent features. This demonstrates the viability of using the voice as a natural means of expressing time series of two features simultaneously. The second experiment consisted of two parts. In a vocal production task, musicians were asked to imitate drum sounds. Listeners were then asked to rate the similarity between the imitations and sounds from the same category (e.g. kick, snare etc.). The results show that drum sounds received the highest similarity ratings when rated against their imitations (as opposed to imitations of another sound), and overall more than half the imitated sounds were correctly identi ed with above chance accuracy from the imitations, although this varied considerably between drum categories. The fi ndings from the vocal imitation experiments highlight the capacity of musicians to vocally imitate musical sounds, and some limitations of non- verbal vocal expression. Finally, we investigated the performance of different audio features as predictors of perceptual similarity between the imitations and imitated sounds from the second experiment. We show that features learned using convolutional auto-encoders outperform a number of popular heuristic features for this task, and that preservation of temporal information is more important than spectral resolution for differentiating between the vocal imitations and same-category drum sounds.Engineering and Physical Sciences Research Council (EP/G03723X/1)