Pitch ability as an aptitude for tone learning

Tone languages such as Mandarin use voice pitch to signal lexical contrasts, presenting a challenge for second/foreign language (L2) learners whose native languages do not use pitch in this manner. The present study examined components of an aptitude for mastering L2 lexical tone. Native English speakers with no previous tone language experience completed a Mandarin word learning task, as well as tests of pitch ability, musicality, L2 aptitude, and general cognitive ability. Pitch ability measures improved predictions of learning performance beyond musicality, L2 aptitude, and general cognitive ability and also predicted transfer of learning to new talkers. In sum, although certain nontonal measures help predict successful tone learning, the central components of tonal aptitude are pitch-specific perceptual measures

Perceptual Musical Features for Interpretable Audio Tagging

In the age of music streaming platforms, the task of automatically tagging music audio has garnered significant attention, driving researchers to devise methods aimed at enhancing performance metrics on standard datasets. Most recent approaches rely on deep neural networks, which, despite their impressive performance, possess opacity, making it challenging to elucidate their output for a given input. While the issue of interpretability has been emphasized in other fields like medicine, it has not received attention in music-related tasks. In this study, we explored the relevance of interpretability in the context of automatic music tagging. We constructed a workflow that incorporates three different information extraction techniques: a) leveraging symbolic knowledge, b) utilizing auxiliary deep neural networks, and c) employing signal processing to extract perceptual features from audio files. These features were subsequently used to train an interpretable machine-learning model for tag prediction. We conducted experiments on two datasets, namely the MTG-Jamendo dataset and the GTZAN dataset. Our method surpassed the performance of baseline models in both tasks and, in certain instances, demonstrated competitiveness with the current state-of-the-art. We conclude that there are use cases where the deterioration in performance is outweighed by the value of interpretability.Comment: Github Repository: https://github.com/vaslyb/perceptible-music-taggin

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings

This paper explores a specific sub-task of cross-modal music retrieval. We consider the delicate task of retrieving a performance or rendition of a musical piece based on a description of its style, expressive character, or emotion from a set of different performances of the same piece. We observe that a general purpose cross-modal system trained to learn a common text-audio embedding space does not yield optimal results for this task. By introducing two changes -- one each to the text encoder and the audio encoder -- we demonstrate improved performance on a dataset of piano performances and associated free-text descriptions. On the text side, we use emotion-enriched word embeddings (EWE) and on the audio side, we extract mid-level perceptual features instead of generic audio embeddings. Our results highlight the effectiveness of mid-level perceptual features learnt from music and emotion enriched word embeddings learnt from emotion-labelled text in capturing musical expression in a cross-modal setting. Additionally, our interpretable mid-level features provide a route for introducing explainability in the retrieval and downstream recommendation processes.Comment: Presented at FIRE 2023 (Forum for Information Retrieval Evaluation) conference, Goa, Indi

A computational framework for sound segregation in music signals

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

OK Computer Analysis: An Audio Corpus Study of Radiohead

The application of music information retrieval techniques in popular music studies has great promise. In the present work, a corpus of Radiohead songs across their career from 1992 to 2017 are subjected to automated audio analysis. We examine findings from a number of granularities and perspectives, including within song and between song examination of both timbral-rhythmic and harmonic features. Chronological changes include possible career spanning effects for a band's releases such as slowing tempi and reduced brightness, and the timbral markers of Radiohead's expanding approach to instrumental resources most identified with the Kid A and Amnesiac era. We conclude with a discussion highlighting some challenges for this approach, and the potential for a field of audio file based career analysis

DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks

Synthetic creation of drum sounds (e.g., in drum machines) is commonly performed using analog or digital synthesis, allowing a musician to sculpt the desired timbre modifying various parameters. Typically, such parameters control low-level features of the sound and often have no musical meaning or perceptual correspondence. With the rise of Deep Learning, data-driven processing of audio emerges as an alternative to traditional signal processing. This new paradigm allows controlling the synthesis process through learned high-level features or by conditioning a model on musically relevant information. In this paper, we apply a Generative Adversarial Network to the task of audio synthesis of drum sounds. By conditioning the model on perceptual features computed with a publicly available feature-extractor, intuitive control is gained over the generation process. The experiments are carried out on a large collection of kick, snare, and cymbal sounds. We show that, compared to a specific prior work based on a U-Net architecture, our approach considerably improves the quality of the generated drum samples, and that the conditional input indeed shapes the perceptual characteristics of the sounds. Also, we provide audio examples and release the code used in our experiments.Comment: 8 pages, 1 figure, 3 tables, accepted in Proc. of the 21st International Society for Music Information Retrieval (ISMIR2020