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The statistical multipitch analyzer described in this paper estimates multiple fundamental frequencies (F0s) in polyphonic music audio signals produced by pitched instruments. It is based on hierarchic4al nonparametric Bayesian models that can deal with uncertainty of unknown random variables such as model complexities (e.g., the number of F0s and the(More)
We aim at enabling a biped robot to interact with humans through real-world music in daily-life environments, e.g., to autonomously keep its steps (stamps) in time with musical beats. To achieve this, the robot should be able to robustly predict the beat times in real time while listening to musical performance with its own ears (head-embedded microphones).(More)
We propose the task of detecting instrumental solos in poly-phonic music recordings, and the usage of a set of four audio features for vocal and instrumental activity detection. Three of the features are based on the prior extraction of the predominant melody line, and have not been used in the context of vocal/instrumental activity detection. Using a(More)
This paper describes a template-matching-based system, called AdaMast, that detects onset times of the bass drum, snare drum, and hi-hat cymbals in polyphonic audio signals of popular songs. AdaMast uses the power spectro-grams of the drum sounds as templates. However, there are two main problems in transcribing drum sounds in the presence of other sounds.(More)
This paper presents an automatic description system of drum sounds for real-world musical audio signals. Our system can represent onset times and names of drums by means of drum descriptors defined in the context of MPEG-7. For their automatic description, drum sounds must be identified in such polyphonic signals. The problem is that acoustic features of(More)
This paper presents a hybrid music recommendation method that solves problems of two prominent conventional methods: collaborative filtering and content-based recommendation. The former cannot recommend musical pieces that have no ratings because recommendations are based on actual user ratings. In addition, artist variety in recommended pieces tends to be(More)
This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists.(More)
This paper describes a public web service for active music listening, Songle, that enriches music listening experiences by using music-understanding technologies based on signal processing. Although various research-level interfaces and technologies have been developed, it has not been easy to get people to use them in everyday life. Songle serves as a(More)
This paper presents a statistical method called Infinite Latent Harmonic Allocation (iLHA) for detecting multiple fundamental frequencies in polyphonic audio signals. Conventional methods face a crucial problem known as model selection because they assume that the observed spectra are superpositions of a certain fixed number of bases (sound sources and/or(More)
We aimed at improving the efficiency and scalability of a hybrid music recommender system based on a proba-bilistic generative model that integrates both collaborative data (rating scores provided by users) and content-based data (acoustic features of musical pieces). Although the hybrid system was proved to make accurate recommendations , it lacks(More)