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스펙트로그램과 심층 신경망을 이용한 온라인 오디오 장르 분류
윤호원(Ho-Won Yun),신성현(Seong-Hyeon Shin),장우진(Woo-Jin Jang),박호종(Hochong Park) 한국방송·미디어공학회 2016 방송공학회논문지 Vol.21 No.6
In this paper, we propose a new method for on-line genre classification using spectrogram and deep neural network. For on-line processing, the proposed method inputs an audio signal for a time period of 1sec and classifies its genre among 3 genres of speech, music, and effect. In order to provide the generality of processing, it uses the spectrogram as a feature vector, instead of MFCC which has been widely used for audio analysis. We measure the performance of genre classification using real TV audio signals, and confirm that the proposed method has better performance than the conventional method for all genres. In particular, it decreases the rate of classification error between music and effect, which often occurs in the conventional method.
장우진(Woo-Jin Jang),윤호원(Ho-Won Yun),신성현(Seong-Hyeon Shin),조효진(Hyo-Jin Cho),장원(Won Jang),박호종(Hochong Park) 한국방송·미디어공학회 2017 방송공학회논문지 Vol.22 No.6
In this paper, we propose a new method for music genre classification using spikegram and deep neural network. The human auditory system encodes the input sound in the time and frequency domain in order to maximize the amount of sound information delivered to the brain using minimum energy and resource. Spikegram is a method of analyzing waveform based on the encoding function of auditory system. In the proposed method, we analyze the signal using spikegram and extract a feature vector composed of key information for the genre classification, which is to be used as the input to the neural network. We measure the performance of music genre classification using the GTZAN dataset consisting of 10 music genres, and confirm that the proposed method provides good performance using a low-dimensional feature vector, compared to the current state-of-the-art methods.