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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...
Mining repeating patterns from music data is one of the most interesting issues of multimedia data mining. However, less work are proposed for mining polyphonic repeating patterns. Hence, two efficient algorithms, A-PRPD (Apriori-based Polyphonic Repeating Pattern Discovery) and T-PRPD (Tree-based Polyphonic Repeating Pattern Discovery), are proposed to discover polyphonic repeating patterns from...
Mining frequent melody structures from music data is one of the most important issues in multimedia data mining. In this paper, we proposed an efficient online algorithm, called BVMDS (bit-vector based mining of data streams), to mine all frequent temporal patterns over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in BVMDS to reduce the time and...
Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs...
In this paper, we propose an efficient algorithm, called MQS-change (changes of music query streams), to detect the changes of maximal melody structures in user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored...
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