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We present a computational model of musical instrument sounds that focuses on capturing the dynamic behavior of the spectral envelope. A set of spectro-temporal envelopes belonging to different notes of each instrument are extracted by means of sinusoidal modeling and subsequent frequency interpolation, before being subjected to principal component analysis. The prototypical evolution of the envelopes...
In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis on the static and transitional information of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. An information fusion approach which integrates both feature level fusion and decision level combination is employed to improve the classification accuracy. Experiments...
This study explores the electroencephalographic (EEG) correlates of emotions during music listening. Principal component analysis (PCA) is used to correlate EEG features with complex music appreciation. This study also applies machine-learning algorithms to demonstrate the feasibility of classifying EEG dynamics in four subjectively-reported emotional states. The high classification accuracy (81.58plusmn3...
Audio data like speech and music can be analyzed and processed with Fourier methods, having as one constraint the constant product of time and frequency resolutions. This problem can be avoided applying the wavelet transform, ensuring good resolutions on both time and frequency supports. We propose in this paper to determine features of music in a combined framework using multi-resolution (wavelet)...
Transcription of music is the process of generating a symbolic representation such as a score sheet or a MIDI file from an audio recording of a piece of music. A statistical machine learning approach for detecting note onsets in polyphonic piano music is presented. An area from the spectrogram of the sound is concatenated into one feature vector. A cascade of boosted classifiers is used for dimensionality...
While many efforts have been made in the audio signal classification field, the noise interruption problem is seldom concerned so far, especially in many telecommunication applications, where a real-time and noise robust approach is needed. This paper addresses this problem by proposing two novel robust features: average pitch density (APD) and relative tonal power density (RTPD). APD refers to the...
This paper discusses an approach to extract constituent percussive bar-long patterns in a music piece given as acoustic signal and to analyze the music structure with a map of constituent rhythmic patterns. Possible applications include music genre classification, music information retrieval (MIR) and music modification such as replacing rhythmic patterns with others. We propose a mathematical method...
We propose a new approach for singer melody extraction, based on blind source separation techniques. The short time Fourier transform (STFT) of the singer signal is modelled by a Gaussian mixture model (GMM) explicitly coupled with a generative source/filter model. We then introduce a simplification of this general GMM and approximate the STFT of the music signal using Non-negative Matrix Factorization...
This paper proposes a tempo feature extraction method based on the long-term modulation spectrum analysis. To transform the modulation spectrum to a condensed feature vector, the log-scale modulation frequency coefficients are introduced. This idea aims at averaging the modulation frequency energy via the constant-Q filter-banks. Further it is pointed out that the feature can be extracted directly...
This study presents a new Speech/Music discrimination method based on spectral peak feature and Multilayer Perceptron. The focus was on feature extraction that reflects spectral peak duration characteristics and high performance using small number of train dataset. Spectral peak features were extracted from audio spectral peak tracks and the feature was normalized by length of segment. Then, we grouping...
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