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Automatic drum transcription methods aim at extracting a symbolic representation of notes played by a drum kit in audio recordings. For automatic music analysis, this task is of particular interest as such a transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In this work, an approach to transcribe drums from polyphonic audio signals based on a re- current neural network is presented. Deep learning techniques like dropout and data augmentation are applied to improve the generalization capabilities of the system.

The problem of detection of impulsive disturbances in archive audio signals is considered. It is shown that semi-causal/noncausal solutions based on joint evaluation of signal prediction errors and leave-one-out signal interpolation errors, allow one to noticeably improve detection results compared to the prediction-only based solutions. The proposed approaches are evaluated on a set of clean audio signals contaminated with real click waveforms extracted from silent parts of old gramophone recordings.

This paper compared the singing voices of four student singers of Chinese national singing before and after vocal warm-up. Statistics showed that the parameters such as deviation from the standard note, vibrato rate and jitter were undergoing significant changes after 30 minutes of warming up exercise, while the differences of vibrato extent demonstrated a controversy result.

This paper compared the singing voices of four student singers of Chinese national singing before and after vocal warm-up. Statistics showed that the parameters such as deviation from the standard note, vibrato rate and jitter were undergoing significant changes after 30 minutes of warming up exercise, while the differences of vibrato extent demonstrated a controversy result.

Live musical performances (e.g., choruses, concerts, and operas) often require the display of lyrics for the convenience of the audience. This requires following the performance and controlling the lyrics display in real time. In practice, this is usually controlled by a staff member of the concert who has been very familiar with the performance. In this paper, we present our effort in implementing a computational system to automate this real-time lyric display process using music following techniques.