Drum Transcription from Polyphonic Music with Instrument-wise Hidden Markov Models
نویسنده
چکیده
This paper describes a system for automatic transcription of drum instruments from polyphonic music signals. For each target drum instrument, a hidden Markov model (HMM) is created to describe the sound characteristics when the instrument is played. Also, a background model with only one state is created for each instrument to describe the sound when the target instrument is not played. The signal is divided into short (2048 samples), overlapping (75%) frames and a set of features is extracted from each frame. The most likely model sequence of sound presence and absence is determined by decoding the instrument-wise HMMs with token passing algorithm.
منابع مشابه
Explicit Duration Hidden Markov Models for Multiple-Instrument Polyphonic Music Transcription
In this paper, a method for multiple-instrument automatic music transcription is proposed that models the temporal evolution and duration of tones. The proposed model supports the use of spectral templates per pitch and instrument which correspond to sound states such as attack, sustain, and decay. Pitch-wise explicit duration hidden Markov models (EDHMMs) are integrated into a convolutive prob...
متن کاملDrum Sound Detection in Polyphonic Music with Hidden Markov Models
This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs). The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat) and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform...
متن کاملMultiple-instrument polyphonic music transcription using a temporally constrained shift-invariant model.
A method for automatic transcription of polyphonic music is proposed in this work that models the temporal evolution of musical tones. The model extends the shift-invariant probabilistic latent component analysis method by supporting the use of spectral templates that correspond to sound states such as attack, sustain, and decay. The order of these templates is controlled using hidden Markov mo...
متن کاملInstrogram: Probabilistic Representation of Instrument Existence for Polyphonic Music
This paper presents a new technique for recognizing musical instruments in polyphonic music. Since conventional musical instrument recognition in polyphonic music is performed notewise, i.e., for each note, accurate estimation of the onset time and fundamental frequency (F0) of each note is required. However, these estimations are generally not easy in polyphonic music, and thus estimation erro...
متن کاملDrum Transcription in Polyphonic Music Using Non-Negative Matrix Factorisation
We present a system that is based on the non-negative matrix factorisation (NMF) algorithm and is able to transcribe drum onset events in polyphonic music. The magnitude spectrogram representation of the input music is divided by the NMF algorithm into source spectra and corresponding time-varying gains. Each of these source components is classified as a drum instrument or non-drum sound and a ...
متن کامل