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This paper proposes a new music genre classification algorithm based on dynamic music frame analysis and support vector machine (SVM). The dynamic music frame analysis could cover the long-term and the short-term music genre features which can represent the time-varying behavior of music signals. The music genre features used in this paper are mel-frequency cepstral coefficient (MFCC) and log energy...
The paper presents a new method for action scene detection in the martial arts movies based on audio feature. First audio information is divided into seven categories: silence, harmonic environmental sound, pure music, pure voice, voice with background music, environmental sound with background music and non-harmonic environmental sound. Then the non-harmonic environmental sound is divided into the...
We present a model-based approach to separating and transcribing single-channel, multi-instrument polyphonic music in a semi-blind fashion. Our system extends the non-negative matrix factorization (NMF) algorithm to incorporate constraints on the basis vectors of the solution. In the context of music transcription, this allows us to encode prior knowledge about the space of possible instrument models...
Human sounds can be roughly divided into two categories: speech and non-speech. Traditional audio scene analysis research puts more emphasis on the classification of audio signals into human speech, music, and environmental sounds. We take a different perspective in this paper. We are mainly interested in the analysis of non-speech human sounds, including laugh, screaming, sneeze, and snore. Toward...
Audio event detection is one of the tasks of the European project VIDIVIDEO. This paper focuses on the detection of non-speech events, and as such only searches for events in audio segments that have been previously classified as non-speech. Preliminary experiments with a small corpus of sound effects have shown the potential of this type of corpus for training purposes. This paper describes our experiments...
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