arXiv subjects

Gakusei Sato

Publications and source records attributed to Gakusei Sato.

2 recordsLinked to original sources

arXiv2025

Time delay embeddings to characterize the timbre of musical instruments using Topological Data Analysis: a study on synthetic and real data

Timbre allows us to distinguish between sounds even when they share the same pitch and loudness, playing an important role in music, instrument recognition, and speech. Traditional approaches, such as frequency analysis or machine learning, often overlook subtle characteristics of sound. Topological Data Analysis (TDA) can capture complex patterns, but its application to timbre has been limited, partly because it is unclear how to represent sound effectively for TDA. In this study, we investigate how different time delay embeddings affect TDA results. Using both synthetic and real audio signals, we identify time delays that enhance the detection of harmonic structures. Our findings show that specific delays, related to fractions of the fundamental period, allow TDA to reveal key harmonic features and distinguish between integer and non-integer harmonics. The method is effective for synthetic and real musical instrument sounds and opens the way for future works, which could extend it to more complex sounds using higher-dimensional embeddings and additional persistence statistics.

cs.SD↗

arXiv2024

Annotation-free Automatic Music Transcription with Scalable Synthetic Data and Adversarial Domain Confusion

Automatic Music Transcription (AMT) is a vital technology in the field of music information processing. Despite recent enhancements in performance due to machine learning techniques, current methods typically attain high accuracy in domains where abundant annotated data is available. Addressing domains with low or no resources continues to be an unresolved challenge. To tackle this issue, we propose a transcription model that does not require any MIDI-audio paired data through the utilization of scalable synthetic audio for pre-training and adversarial domain confusion using unannotated real audio. In experiments, we evaluate methods under the real-world application scenario where training datasets do not include the MIDI annotation of audio in the target data domain. Our proposed method achieved competitive performance relative to established baseline methods, despite not utilizing any real datasets of paired MIDI-audio. Additionally, ablation studies have provided insights into the scalability of this approach and the forthcoming challenges in the field of AMT research.

cs.SD↗