Taking Synchrony Seriously: A Perceptual-Level Model of Infant Synchrony Detection

Synchrony detection between different sensory and/or motor channels appears critically important for young infant learning and cognitive development. For example, empirical studies demonstrate that audio-visual synchrony aids in language acquisition. In this paper we compare these infant studies with a model of synchrony detection based on the Hershey and Movellan (2000) algorithm augmented with methods for quantitative synchrony estimation. Four infant-model comparisons are presented, using audio-visual stimuli of increasing complexity. While infants and the model showed learning or discrimination with each type of stimuli used, the model was most successful with stimuli comprised of one audio and one visual source, and also with two audio sources and a dynamic-face visual motion source. More difficult for the model were stimuli conditions with two motion sources, and more abstract visual dynamics—an oscilloscope instead of a face. Future research should model the developmental pathway of synchrony detection. Normal audio-visual synchrony detection in infants may be experience-dependent (e.g., Bergeson, et al., 2004)

Corpus-Based Transcription as an Approach to the Compositional Control of Timbre

Timbre space is a cognitive model useful to address the problem of structuring timbre in electronic music. The recent concept of corpus-based concatenative sound synthesis is proposed as an approach to timbral control in both real- and deferred-time applications. Using CataRT and related tools in the FTM and Gabor libraries for Max/MSP we describe a technique for real-time analysis of a live signal to pilot corpus-based synthesis, along with examples of compositional realizations in works for instruments, electronics, and sound installation. To extend this technique to computer-assisted composition for acoustic instruments, we develop tools using the Sound Description Interchange Format (SDIF) to export sonic descriptors to OpenMusic where they may be further manipulated and transcribed into an instrumental score. This presents a flexible technique for the compositional organization of noise-based instrumental sounds

A Linear Hybrid Sound Generation of Musical Instruments using Temporal and Spectral Shape Features

The generation of a hybrid musical instrument sound using morphing has always been an area of great interest to the music world. The proposed method exploits the temporal and spectral shape features of the sound for this purpose. For an effective morphing the temporal and spectral features are found as they can capture the most perceptually salient dimensions of timbre perception, namely, the attack time and the distribution of spectral energy. A wide variety of sound synthesis algorithms is currently available. Sound synthesis methods have become more computationally efficient. Wave table synthesis is widely adopted by digital sampling instruments or samplers. The Over Lap Add method (OLA) refers to a family of algorithms that produce a signal by properly assembling a number of signal segments. In granular synthesis sound is considered as a sequence with overlaps of elementary acoustic elements called grains. The simplest morph is a cross-fade of amplitudes in the time domain which can be obtained through cross synthesis. A hybrid sound is generated with all these methods to find out which method gives the most linear morph. The result will be evaluated as an error measure which is the difference between the calculated and interpolated features. The extraction of morph in a perceptually pleasant manner is the ultimate requirement of the work. DOI: 10.17762/ijritcc2321-8169.16045

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

A computational framework for sound segregation in music signals

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Harmonic Change Detection from Musical Audio

In this dissertation, we advance an enhanced method for computing Harte et al.’s [31] Harmonic Change Detection Function (HCDF). HCDF aims to detect harmonic transitions in musical audio signals. HCDF is crucial both for the chord recognition in Music Information Retrieval (MIR) and a wide range of creative applications. In light of recent advances in harmonic description and transformation, we depart from the original architecture of Harte et al.’s HCDF, to revisit each one of its component blocks, which are evaluated using an exhaustive grid search aimed to identify optimal parameters across four large style-specific musical datasets. Our results show that the newly proposed methods and parameter optimization improve the detection of harmonic changes, by 5.57% (f-score) with respect to previous methods. Furthermore, while guaranteeing recall values at > 99%, our method improves precision by 6.28%. Aiming to leverage novel strategies for real-time harmonic-content audio processing, the optimized HCDF is made available for Javascript and the MAX and Pure Data multimedia programming environments. Moreover, all the data as well as the Python code used to generate them, are made available.<br /

Generative rhythmic models

A system for generative rhythmic modeling is presented. The work aims to explore computational models of creativity, realizing them in a system designed for realtime generation of semi-improvisational music. This is envisioned as an attempt to develop musical intelligence in the context of structured improvisation, and by doing so to enable and encourage new forms of musical control and performance; the systems described in this work, already capable of realtime creation, have been designed with the explicit intention of embedding them in a variety of performance-based systems. A model of qaida, a solo tabla form, is presented, along with the results of an online survey comparing it to a professional tabla player's recording on dimensions of musicality, creativity, and novelty. The qaida model generates a bank of rhythmic variations by reordering subphrases. Selections from this bank are sequenced using a feature-based approach. An experimental extension into modeling layer- and loop-based forms of electronic music is presented, in which the initial modeling approach is generalized. Starting from a seed track, the layer-based model utilizes audio analysis techniques such as blind source separation and onset-based segmentation to generate layers which are shuffled and recombined to generate novel music in a manner analogous to the qaida model.M.S.Committee Chair: Chordia, Parag; Committee Member: Freeman, Jason; Committee Member: Weinberg, Gi

Real-time Sound Source Separation For Music Applications

Sound source separation refers to the task of extracting individual sound sources from some number of mixtures of those sound sources. In this thesis, a novel sound source separation algorithm for musical applications is presented. It leverages the fact that the vast majority of commercially recorded music since the 1950s has been mixed down for two channel reproduction, more commonly known as stereo. The algorithm presented in Chapter 3 in this thesis requires no prior knowledge or learning and performs the task of separation based purely on azimuth discrimination within the stereo field. The algorithm exploits the use of the pan pot as a means to achieve image localisation within stereophonic recordings. As such, only an interaural intensity difference exists between left and right channels for a single source. We use gain scaling and phase cancellation techniques to expose frequency dependent nulls across the azimuth domain, from which source separation and resynthesis is carried out. The algorithm is demonstrated to be state of the art in the field of sound source separation but also to be a useful pre-process to other tasks such as music segmentation and surround sound upmixing