Download A Matlab Toolbox for Musical Feature Extraction from Audio We present MIRtoolbox, an integrated set of functions written in Matlab, dedicated to the extraction of musical features from audio files. The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms, and integrating different variants proposed by alternative approaches – including new strategies we have developed –, that users can select and parametrize. This paper offers an overview of the set of features, related, among others, to timbre, tonality, rhythm or form, that can be extracted with MIRtoolbox. Four particular analyses are provided as examples. The toolbox also includes functions for statistical analysis, segmentation and clustering. Particular attention has been paid to the design of a syntax that offers both simplicity of use and transparent adaptiveness to a multiplicity of possible input types. Each feature extraction method can accept as argument an audio file, or any preliminary result from intermediary stages of the chain of operations. Also the same syntax can be used for analyses of single audio files, batches of files, series of audio segments, multichannel signals, etc. For that purpose, the data and methods of the toolbox are organised in an object-oriented architecture.
Download Multi-feature modeling of pulse clarity: Design, validation, and optimisation Pulse clarity is considered as a high-level musical dimension that conveys how easily in a given musical piece, or a particular moment during that piece, listeners can perceive the underlying rhythmic or metrical pulsation. The objective of this study is to establish a composite model explaining pulse clarity judgments from the analysis of audio recordings, decomposed into a set of independent factors related to various musical dimensions. To evaluate the pulse clarity model, 25 participants have rated the pulse clarity of one hundred excerpts from movie soundtracks. The mapping between the model predictions and the ratings was carried out via regressions. More than three fourth of listeners’ rating variance can be explained with a combination of periodicity-based and nonperiodicity-based factors.