Towards Automatic Expressive Pipa Music Transcription Using Morphological Analysis of Photoelectric Signals

TL;DR

This paper introduces a new method for transcribing pipa music by analyzing optical sensor signals, improving pitch estimation and computational efficiency.

Contribution

A novel time–frequency feature called continuous time-period mapping (CTPM) is developed for pipa music transcription.

Findings

The proposed onset detection method outperformed short-time methods during tremolo techniques.

The zero-crossing-based pitch estimator achieved comparable accuracy with better computational efficiency.

The method works well for playing techniques involving pitch shifts and tremolo.

Abstract

The musical signal produced by plucked instruments often exhibits non-stationarity due to variations in the pitch and amplitude, making pitch estimation a challenge. In this paper, we assess different transcription processes and algorithms applied to signals captured by optical sensors mounted on a pipa—a traditional Chinese plucked instrument—played using a range of techniques. The captured signal demonstrates a distinctive arched feature during plucking. This facilitates onset detection to avoid the impact of the spurious energy peaks within vibration areas that arise from pitch-shift playing techniques. Subsequently, we developed a novel time–frequency feature, known as continuous time-period mapping (CTPM), which contains pitch curves. The proposed process can also be applied to playing techniques that mix pitch shifts and tremolo. When evaluated on four renowned pipa music pieces of varying difficulty levels, our fully time-domain-based onset detectors outperformed four short-time methods, particularly during tremolo. Our zero-crossing-based pitch estimator achieved a performance comparable to short-time methods with a far better computational efficiency, demonstrating its suitability for use in a lightweight algorithm in future work.

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