Musical Instrument Classification Using Embedded Hidden Markov Models
نویسنده
چکیده
In this paper, a novel method for recognition of musical instruments in a polyphonic music is presented by using an embedded hidden Markov model (EHMM). EHMM is a doubly embedded HMM structure where each state of the external HMM is an independent HMM. The classification is accomplished for two different internal HMM structures where GMMs are used as likelihood estimators for the internal HMMs. The results are compared to those achieved by an artificial neural network with two hidden layers. Appropriate classification accuracies were achieved both for solo instrument performance and instrument combinations which demonstrates that the new approach outperforms the similar classification methods by means of the dynamic of the signal. Keywords—hidden Markov model (HMM), embedded hidden Markov models (EHMM), MFCC, musical instrument.
منابع مشابه
Instrument classification using Hidden Markov Models
In this paper we present first results on musical instrument classification using an HMM based recognizer. The final goal of our work is to automatically evaluate instruments and to classify them according to their characteristics. The first step in this direction was to train a system that is able to recognize a particular instrument among others of the same kind (e.g. guitars). The recognitio...
متن کاملComparison of Subspace Analysis-based and Statistical Model-based Algorithms for Musical Instrument Classification
In this paper, three classes of algorithms for automatic classification of individual musical instrument sounds are compared. The first class of classifiers is based on Non-negative Matrix Factorization, the second class of classifiers employs automatic feature selection and Gaussian Mixture Models and the third is based on continuous Hidden Markov Models. Several perceptual features used in ge...
متن کاملSpectrogram Based Musical Instrument Identification Using Hidden Markov Model (hmm) for Monophonic and Polyphonic Music Signals
Spectrogram is generated for musical notes, which is used to calculate the spectral, temporal and modulation features. To detect the musical instruments from polyphonic and monophonic musical notes , 23 features are analyzed . Out of 23 features 12 specific features are used to generate feature vector . Hidden Markov model (HMM) is used to calculate the conditional instrument existence probabil...
متن کاملHidden Markov Model based Recognition of Musical Pattern in South Indian Classical Music
Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno musicological research and can become an indispensable tool for the search and comparison of music extracts within a large multimedia database. This paper finds an efficient method for recognizing isolated musical patterns in a monophonic environment, using Hidden Markov Model. Each pattern, to be recogniz...
متن کاملMusic Transcription with ISA and HMM
We propose a new generative model for polyphonic music based on nonlinear Independent Subspace Analysis (ISA) and factorial Hidden Markov Models (HMM). ISA represents chord spectra as sums of note power spectra and note spectra as sums of instrument-dependent log-power spectra. HMM models note duration. Instrument-dependent parameters are learnt on solo excerpts and used to transcribe musical r...
متن کامل