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A musical chord is usually described by its root note and the chord type. While a substantial amount of work has been done in the field of music information retrieval (MIR) to automate chord recognition, the role of root notes in this task has seldom received specific attention. In this paper, we present a new approach and empirical studies demonstrating improved accuracy in chord recognition by properly...
Evaluation is important to assess the performance of a computer system in fulfilling a certain user need. In the context of recommendation, researchers usually evaluate the performance of a recommender system by holding out a random subset of observed ratings and calculating the accuracy of the system in reproducing such ratings. This evaluation strategy, however, does not consider the fact that in...
There has been an increasing attention on learning feature representations from the complex, high-dimensional audio data applied in various music information retrieval (MIR) problems. Unsupervised feature learning techniques, such as sparse coding and deep belief networks have been utilized to represent music information as a term-document structure comprising of elementary audio codewords. Despite...
Unsupervised feature learning algorithms such as sparse coding and deep belief networks have been shown a viable alternative to hand-crafted feature design for music information retrieval. Nevertheless, such algorithms are usually computationally expensive. This paper investigates techniques to accelerate sparse feature extraction and music classification. To study the trade-off between computational...
Clustering for better representation of the diversity of text or image search results has been studied extensively. In this paper, we extend this methodology to the novel domain of music search. We conduct empirical evaluation of different clustering algorithms, audio feature representations, and the incorporation of lyrics for music clustering. Our evaluation shows the fusion of audio and text features...
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