This paper presents the main ideas behind ctfr, an extensible, user-friendly Python package for efficiently combining time-frequency representations (TFRs) of audio signals into a single representation that captures the best aspects of each, achieving high resolutions in both time and frequency. The authors develop and evaluate algorithmic tweaks and approximation schemes for existing TFR combination methods, with significant performance improvements over baseline implementations. In addition, combined TFRs are employed in training a deep learning system for note transcription from audio performances, showing improved results over traditional TFRs, thus demonstrating the effectiveness of using combination methods in audio processing and music information retrieval pipelines.
(See document for exact affiliation information.)
https://doi.org/10.17743/jaes.2022.0266
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
