Automatic music transcription from audio has long been one of the most intriguing problems and a challenge in the field of music information retrieval, because it requires a series of low-level tasks such as onset/offset detection and F0 estimation, followed by high-level post-processing for symbolic representation. In this paper, a comprehensive transcription system for monophonic singing voice based on harmonic structure analysis is proposed. Given a precise tracking of the fundamental frequency, a novel acoustic feature is derived to signify the harmonic structure in singing voice signals, regardless of the loudness and pitch. It is then used to generate a parametric mixture model based on the von Mises-Fisher distribution, so that the model represents the intrinsic harmonic structures within a region of smoothly connected notes. To identify the note boundaries, the local homogeneity in the harmonic structure is exploited by two different methods: the self-similarity analysis and hidden Markov model. The proposed system identifies the note attributes including the onset time, duration and note pitch. Evaluations are conducted from various aspects to verify the performance improvement of the proposed system and its robustness, using the latest evaluation methodology for singing transcription. The results show that the proposed system significantly outperforms other systems including the state-of-the-art systems.

In this report, we propose a tracking algorithm of speaker direction using microphones located at vertices of an equilateral triangle. The method realizes tracking by minimizing a performance index that consists of the cross spectra at three different microphone pairs in the triangular array. We adopt the steepest descent method to minimize it, and for guaranteeing global convergence to the correct direction with high accuracy, we alter the performance index during the adaptation depending on the convergence state. Through some computer simulation and experiments in a real acoustic environment, we show the effectiveness of the proposed method.

The proposed DOA (Direction Of Arrival) estimation method by integrating the frequency array data generated from microphone pairs in an equilateral-triangular microphone array is extended here. The method uses four microphones located at the apices of regular tetrahedron to enable to estimate the elevation angle from the array plane as well. Furthermore, we introduce an idea for separate estimation of azimuth and elevation to reduce the computational loads.

In this paper, we propose a DOA (Direction Of Arrival) estimation method of speech signal using three microphones. The angular resolution of the method is almost uniform with respect to DOA. Our previous DOA estimation method using the frequency-domain array data for a pair of microphones achieves high precision estimation. However, its resolution degrades as the propagating direction being apart from the array broadside. In the method presented here, we utilize three microphones located at vertices of equilateral triangle and integrate the frequency-domain array data for three pairs of microphones. For the estimation scheme, the subspace analysis for the integrated frequency array data is proposed. Through both computer simulations and experiments in a real acoustical environment, we show the efficiency of the proposed method.