Drum sound identification for polyphonic music using template adaptation and matching methods
نویسندگان
چکیده
This paper describes drum sound identification for polyphonic musical audio signals. It is difficult to identify drum sounds in such signals because acoustic features of those sounds vary with each musical piece and precise templates for them cannot be prepared in advance. To solve this problem, we propose new template-adaptation and templatematching methods. The former method adapts a single seed template prepared for each kind of drums to the corresponding drum sound appearing in an actual musical piece containing sounds of various musical instruments. The latter method then uses a carefully-designed distance measure that can detect all the onset times of each drum in the same piece by using the corresponding adapted template. The onset times of bass and snare drums in any piece can thus be identified even if their timbres are different from prepared templates. Experimental results with our methods showed that the accuracy of identifying bass and snare drums in popular music was about 90%.
منابع مشابه
Automatic Drum Sound Description for Real-World Music Using Template Adaptation and Matching Methods
This paper presents an automatic description system of drum sounds for real-world musical audio signals. Our system can represent onset times and names of drums by means of drum descriptors defined in the context of MPEG-7. For their automatic description, drum sounds must be identified in such polyphonic signals. The problem is that acoustic features of drum sounds vary with each musical piece...
متن کاملAdaMast: A Drum Sound Recognizer based on Adaptation and Matching of Spectrogram Templates
This paper describes a template-matching-based system, called AdaMast, that detects onset times of the bass drum, snare drum, and hi-hat cymbals in polyphonic audio signals of popular songs. AdaMast uses the power spectrograms of the drum sounds as templates. However, there are two main problems in transcribing drum sounds in the presence of other sounds. The first problem is that actual drum-s...
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 کاملDrum Detection from Polyphonic Audio via Detailed Analysis of the Time Frequency Domain
This publication presents a method for the automatic detection and classification of three distinct drum instruments in real world musical signals. The regarded instruments are kick, snare and hi-hat as agreed by the participants of the contest category Audio Drum Detection within the 2nd Annual Music Information Retrieval Evaluation eXchange (MIREX 2005). There are two challenging issues inher...
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