Detecting harmonic change in musical audio

By C. Harte, Mark B. Sandler, Martin Gasser. AMCMM '06, 2006

309 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks (2017, background): “• TD: or tonal distance (Harte, Sandler, and Gasser 2006).”
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment (2017, background): “• TD : or tonal distance (Harte, Sandler, and Gasser 2006).”
Content-Based Music Information Retrieval: Current Directions and Future Challenges (2008, background): “About the same time, Harteet al. [ 55 ] proposed a new 6-D feature vectorVcalled tonal centroidVbased on harmonic relationships in western music.”
FMA: A Dataset for Music Analysis (2016, unknown): “1 Chroma [11] 84 44 44 48 49 2 Tonnetz [14] 42 40 37 42 41 3 MFCC [33] 140 58 55 61 53 4 Spec.”
Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres (2011, background): “Harmonic Change Detection Function (HCDF) indicates the tonal diversity along the time (Harte et al., 2006) and Key Clarity, based also on chromagram, compares the ensuing tonal profile to preestablished key profiles representing major and minor keys (Gomez, 2006).”