Music separation method of MFCC (Mel Frequency Cepstrum Coefficient)-multi-repetition model in combination with HPSS (Harmonic/Percussive Sound Separation)

A music and background music technology, applied in speech analysis, electroacoustic musical instruments, instruments, etc., can solve the problems of poor music separation adaptability

Inactive Publication Date: 2015-05-13
CHONGQING UNIV OF POSTS & TELECOMM
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However, the separation method based on repeated features has only been developed. For different types of sound sources, t

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  • Music separation method of MFCC (Mel Frequency Cepstrum Coefficient)-multi-repetition model in combination with HPSS (Harmonic/Percussive Sound Separation)
  • Music separation method of MFCC (Mel Frequency Cepstrum Coefficient)-multi-repetition model in combination with HPSS (Harmonic/Percussive Sound Separation)
  • Music separation method of MFCC (Mel Frequency Cepstrum Coefficient)-multi-repetition model in combination with HPSS (Harmonic/Percussive Sound Separation)

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[0027] As mentioned above, the traditional repetitive structure separation method is easy to miss the music information with a relatively gentle rhythm, because the extraction of the repetitive structure adopts the "rhythm spectrum" method, which is easy to capture the sound source information with obvious rhythm changes, but It is easy to miss the more soothing sound sources produced by orchestral instruments (such as flute, violin, piano, etc.). In this regard, the harmonic source separation is added before the separation. Here, the harmonic and impact source separation method HPSS is used to analyze the sound source first, and then separate it, so as to effectively improve the separation performance.

[0028] Generally speaking, the spectrogram of a music signal usually presents two forms of distribution, one is a continuous and smooth distribution along the time axis, and the other is a continuous and smooth distribution along the frequency axis. The sound sources of these ...

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Abstract

The invention discloses a music separation method of an MFCC (Mel Frequency Cepstrum Coefficient)-multi-repetition model in combination with an HPSS (High Performance Storage System), and relates to the technical field of signal processing. In consideration of high probability of ignore of a gentle sound source and time-varying change characteristic of music, the sound source type is analyzed through a harmonic/percussive sound separation (HPSS) method to separate out a harmonic source, then MFCC characteristic parameters of the remaining sound sources are extracted, and similar operation is performed on the sound sources to construct a similar matrix so as to establish a multi-repetition structural model of the sound source suitable for tune transformation, so that a mask matrix is obtained, and finally the time domain waveform of a song and background music is obtained through ideal binary mask (IBM) and fourier inversion. According to the method, effective separation can be performed on different types of sound source signals, so the separation precision is improved; meanwhile the method is low in complexity, high in processing speed and higher in stability, and has broad application prospect in the fields such as singer retrieval, song retrieval, melody extraction and voice recognition in a musical instrument background.

Description

Technical field: [0001] The invention relates to audio signal processing, in particular to the separation of singing voice and background music in music signals. Background technique [0002] Extracting the background music and singing voice of music has broad application prospects in the field of audio signal processing, such as singer retrieval and song retrieval, melody extraction, speech recognition in the background of musical instruments, etc., but audio separation is also a very challenging task. On the problem of polyphonic music separation, the auditory system of the human ear has an incredible ability. People can easily distinguish the singing voice in the music from the background music, and even distinguish which instruments are included in the tune. These are easy things, but For computers, however, there are many difficulties. [0003] Today's music separation techniques are mainly statistical techniques and research techniques based on psychoacoustics. Stati...

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IPC IPC(8): G10L21/028G10L25/81
CPCG10H1/00G10H2210/041G10H2210/056G10H2250/235G10L25/81
Inventor 张天骐徐昕张刚高超阳锐李灿
Owner CHONGQING UNIV OF POSTS & TELECOMM
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