Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function.
This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.
Graduate School of System Informatics, Kobe University, 1-1 Rokkodai, Kobe, 657-8501 Japan. takashima@me.cs.scitec.kobe-u.ac.jp
Ryoichi
R
Takashima
Tetsuya
T
Takiguchi
Yasuo
Y
Ariki
D000162:Acoustics / Q000295:instrumentation; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016002:Discriminant Analysis; D005160:Facility Design and Construction; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D009038:Motion; D009622:Noise / Q000009:adverse effects; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013061:Speech Acoustics; D013997:Time Factors; D014159:Transducers; D014732:Vibration