MIR in Matlab (II): A Toolbox for Musical Feature Extraction from Audio

By O. Lartillot, Petri Toiviainen. International Society for Music Information Retrieval Conference, 2007

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Chunking in Music by Coarticulation (2010, methodology): “This was then exported to Matlab, and various position, velocity, and acceleration data was then calculated and correlated with the tone onset data from spectrograms of the sound files using the MIR and MoCap Toolboxes (Lartillot & Toiviainen 2007, Toiviainen 2008).”
Single chords convey distinct emotional qualities to both naïve and expert listeners (2016, methodology): “…of chords in the present study, we extracted a set of core features implicated in past studies of timbre and emotions (Eerola et al., 2012), namely roughness, brightness, irregularity, spectral flux and log attack time from all stimuli using MIR toolbox (version 1.5, Lartillot & Toiviainen, 2007).”
Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres (2011, methodology): “A number of efficient tools have been created that allow for the rich processing of audio (Tzanetakis & Cook, 2000; Leman, Lesaffre, & Tanghe, 2001; Lartillot & Toiviainen, 2007).”
An open access database for the evaluation of respiratory sound classification algorithms (2019, methodology): “Then, using the MIR Toolbox (Lartillot and Toiviainen 2007), we extracted 13 MFCCs in 10 ms frames with 5 ms overlap (due to the low stationarity of the signal) and computed their mean for each respiratory cycle (event, in the case of experiment 2, as discussed below).”