Wenshan Liou

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This paper proposes the use of GPU (graphic processing unit) to implementing a two-stage comparison method for a QBSH (query by singing/humming) system. The system can take a user's singing or humming and retrieve the top-10 most likely candidates from a database of 8431 songs. In order to speed up the comparison, we apply linear scaling in the first stage(More)
This paper presents the use of GPUs (graphic processing units) for implementing an efficient audio fingerprinting (AFP) system for audio music retrieval. Such a music retrieval system can compare a 10-second recording of exact but noisy audio clip to the database of more than 100K songs on a single PC with GPU cards. Due to the use of GPUs, we can achieve a(More)
Automated symbolic music alignment is a challenging task due to the variation of performance by different performers. It becomes more complicated when dealing with polyphonic music because note events could occur at the same time. The goal of this study is to find an efficient algorithm for aligning two polyphonic symbolic representations (MIDI files, for(More)
This paper presents an effective re-ranking method that uses learning-to-rank paradigms to improve the accuracy of landmark-based audio fingerprinting (AFP) for audio music retrieval. The re-ranking mechanism is invoked whenever the returned ranking from an AFP system does not have a high enough confidence measure. We propose that use of new features for(More)
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