dataset

Description

While the MIR-1K dataset is the first public dataset specifically created for singing voice separation, rapid advances in computing technology demand even higher quality datasets. Thus, we construct a new publicly available iKala dataset containing:

  • Wavfile: 252 audio clips of 30s each (44.1kHz, 16-bit WAV format with music and voice recorded at the left and right channels respectively)
  • PitchLabel: pitch contour annotations (32ms frames, LAB format with continuous MIDI pitches or 0 for non-vocal breaks; convertible to JAMS with ikala_melody_parser.py)
  • Lyrics: lyrics with timestamps (LAB format with start time in ms, end time in ms, word, and pronunciation if Chinese; convertible to JAMS with ikala_lyrics_parser.py)

Each clip is named in the form SongId_ClipId (ClipId can be verse or chorus). These clips are sampled from 206 iKala songs featuring professional singers and musicians. Please see the following table for a detailed comparison between the two datasets:

  MIR-1K iKala
Sampling rate
Singer quality
Clip duration
Number of clips
16kHz
Amateur
4-13s
1,000
44.1kHz
Professional
30s
252
Voice recorded separately
Pitch contour annotations
Yes
Yes
Yes
Yes
Voice type annotations
Lyrics with speech
Yes
Yes
No
No
Lyrics with timestamps
Separate chorus and verse
Instrumental solo
No
No
No
Yes
Yes
Yes

Pronunciations

For Chinese lyrics (except those sung in Taiwanese, see table below), pronunciations in IPA are provided to assist lyrics-informed separation. Thanks to Lien-Chiao Lin and Hung-Shin Lee for their contributions.

Taiwanese songs 21031_verse
21038_chorus
21038_verse
21060_verse
21069_verse
31081_verse
31083_chorus
31083_verse
31093_chorus
45392_chorus
45392_verse
45412_chorus
45412_verse
45416_chorus
45416_verse
54186_verse
54202_chorus

Singer Information

iKala hired six studio singers to sing the songs. The singer to song ID mapping can be found in id_mapping.txt. This information may be used to develop singer-independent or (supervised) singer-dependent models for source separation.