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We address a novel nonnegative matrix factorization (NMF) with a new basis deformation method to handle various music sounds. Conventional supervised NMF has a critical problem that a mismatch between bases trained in advance and an actual target sound reduces the accuracy of separation. To solve this problem, we proposed an advanced supervised NMF that applies a single time-invariant filter to the...
We propose novel methods for automatically detecting non-stationary segments using non-negative matrix factorization (NMF) with aiming to effective sound annotation. For acoustic event detection or acoustic scene analysis, preparing a sufficient amount of training data is important. However, listening to all recorded signals and annotating them are very time-consuming. Assuming that the observed acoustic...
Given the increasing attention paid to speech emotion classification in recent years, this work presents a novel speech emotion classification approach based on the multiple kernel Gaussian process. Two major aspects of a classification problem that play an important role in classification accuracy are addressed, i.e. feature extraction and classification. Prosodic features and other features widely...
Non-negative matrix factorization (NMF) is attracting a lot of attention as a powerful technique for music transcription and audio source separation. With this approach, the magnitude (or power) spectrogram of a mixed signal, interpreted as non-negative matrix Y, is factorized into the product of two non-negative matrices, dictionary matrix H and activation matrix U. Each template vector in the dictionary...
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