RWC Music Database: Popular, Classical and Jazz Music Databases

By Masataka Goto, Hiroki Hashiguchi, T. Nishimura, R. Oka. International Society for Music Information Retrieval Conference, 2002

755 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/de31aa8e914c60189a425e174af223967e2722cc

How researchers see this paper

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

Citations in the corpus

Crepe: A Convolutional Representation for Pitch Estimation (2018, methodology): “We use two such datasets: the ﬁrst, RWC-synth, contains 6.16 hours of audio synthesized from the RWC Music Database [24] and is used to evaluate pYIN in [13].”
Segmentation (2019, unknown): “Speciﬁcally, our unique kernel (also known as segmentation head) is dynamically generated basing on the global features of the input image, and generates one polyp segmentation prediction in each decoder stage.”
Fundamentals of Music Processing (2015, background): “For example, timbre information allows us to tell apart the sounds produced by the oboe and the violin, even when the pitch and loudness of the sounds are identical [20].”
Automatic music transcription: challenges and future directions (2013, methodology): “Except for the case of solo piano where available data include the MAPS database [43] and the Disklavier piano dataset [107], although the latter is synthesized from MIDI files extracted from the Disklavier performance, data sets currently employed for evaluation are small: a subset of the RWC datab”
Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings (2013, methodology): “3), the basis functions are estimated using the RWC musical instrument sound database [48,49] and the full pitch range for each instrument.”
FMA: A Dataset for Music Analysis (2016, background): “…MIR has hindered research on data-heavy models such as DL. Table 1 lists the most common datasets used for dataset 1 #clips #artists year audio RWC [12] 465 - 2001 yes CAL500 [45] 500 500 2007 yes Ballroom [13] 698 - 2004 yes GTZAN [46] 1,000 ∼ 300 2002 yes MusiClef [36] 1,355 218 2012 yes…”
PYIN: A fundamental frequency estimator using probabilistic threshold distributions (2014, methodology): “We synthesised singing from the F0 pitch tracks available with the RWC Music Database [12] and saved them as linear PCM wav files at a sample rate of 44.”
RWC Music Database: Music genre database and musical instrument sound database (2003, methodology): “We have described the construction of the Music Genre Database and Musical Instrument Sound Database complementing the four original DBs (Goto et al., 2002) in the RWC Music Database.”