Pitch, together with other midlevel music features such as rhythm and timbre, holds the promise of bridging the semantic gap between low-level features and high-level semantics for music understanding. This paper investigates the pitch estimation of a piano music signal by exemplar-based sparse representation. A note exemplar is a segment of a piano note, stored in the dictionary. We first describe how to represent a segment of the piano music signal as a linear combination of a small number of note exemplars from a large note exemplar dictionary and then show how the sparse representation problem can be solved by -regularized minimization. The proposed approach incorporates tuning factor estimation, note candidate selection, and hidden-Markov-model-based smoothing into the estimation process to improve accuracy. Unlike previous approaches, the proposed approach does not require retraining for a new piano. Instead, only a dozen notes of the new piano are needed. This feature is computationally attractive and avoids intense manual labeling. The system performance is evaluated using 70 classical music recordings of two real pianos under different recording conditions. The results show that the proposed system outperforms four state-of-the-art systems.

Assuming that the waveforms of piano notes are pre-stored and that the magnitude spectrum of a piano signal segment can be represented as a linear combination of the magnitude spectra of the pre-stored piano waveforms, we formulate the automatic transcription of polyphonic piano music as a sparse representation problem. First, the note candidates of the piano signal segment are found by using heuristic rules. Then, the sparse representation problem is solved by l1-regularized minimization, followed by temporal smoothing the frame-level results based on hidden Markov models. Evaluation against three state-of-the-art systems using ten classical music recordings of a real piano is performed to show the performance improvement of the proposed system.

The rich emotion part of a drama video is often the center of attraction to the viewer. Emotion-based highlights extraction is useful for applications such as drama video retrieval and automatic trailer generation. In this paper, we propose a system that uses music emotion and human face as features for automatic extraction of the emotion highlights of a drama video. These high-level audiovisual features are used because music invokes emotion response from the viewer and characters express emotion on their faces. To avoid the interference of speech signal and environmental noise, a novel two-stage music emotion recognition scheme is developed. We first detect the presence of incidental music in a drama video using an audio fingerprint technique, and then perform emotion recognition on the noise-free music available from the album of the incidental music. This simple but effective approach greatly improves the accuracy of music emotion recognition. Besides the conventional subjective evaluation, we propose a new metric for quantitative performance evaluation of highlights extraction. Evaluation results are provided to illustrate the performance of the system.