Next week, the MTG is participating in the Workshop on Detection and Classification of Acoustic Scenes and Events, DCASE 2024. We will present the following papers at the workshop:

Panagiota Anastasopoulou, Jessica Torrey, Xavier Serra, and Frederic Font.  "Heterogeneous Sound Classification with the _Broad Sound_ Taxonomy and Dataset".

Automatic sound classification has a wide range of applications in machine listening, enabling context-aware sound processing and understanding. This paper explores methodologies for automatically classifying heterogeneous sounds characterized by high intraclass variability. Our study evaluates the classification task using the Broad Sound Taxonomy, a two-level taxonomy comprising 28 classes designed to cover a heterogeneous range of sounds with semantic distinctions tailored for practical user applications. We construct a dataset through manual annotation to ensure accuracy, diverse representation within each class and relevance in real-world scenarios. We compare a variety of both traditional and modern machine learning approaches to establish a baseline for the task of heterogeneous sound classification. We investigate the role of input features, specifically examining how acoustically derived sound representations compare to embeddings extracted with pre-trained deep neural networks that capture both acoustic and semantic information about sounds. Experimental results illustrate that audio embeddings encoding acoustic and semantic information achieve higher accuracy in the classification task. After careful analysis of classification errors, we identify some underlying reasons for failure and propose actions to mitigate them. The paper highlights the need for deeper exploration of all stages of classification, understanding the data and adopting methodologies capable of effectively handling data complexity and generalizing in real-world sound environments.

Amaia Sagasti, Martín Rocamora, and Frederic Font. "Prediction of Pleasantness and Eventfulness Perceptual Sound Qualities in Urban Soundscapes". 

The acoustic environment induces emotions in human listeners. To describe such emotions, ISO-12913 defines pleasantness and eventfulness as orthogonal properties that characterise urban soundscapes. In this paper, we study different approaches for automatically estimating these two perceptual sound qualities. We emphasize the comparison of three sets of audio features: a first set from the acoustic and psychoacoustic domain, suggested in ISO-12913; a second set of features from the machine listening domain based on traditional signal processing algorithms; and a third set consisting of audio embeddings generated with a pre-trained audio-language deep-learning model. Each feature set is tested on its own and in combination with ground-truth labels about the sound sources present in the recordings to determine if this additional information improves the prediction accuracy. Our findings indicate that the deep-learning representation yields slightly better performance than the other feature sets when predicting pleasantness, but all of them yield similar performance when predicting eventfulness. Nevertheless, deep-learning embeddings present other advantages, such as faster calculation times and greater robustness against changes in sensor calibration, making them more effective for real-time acoustic monitoring. Furthermore, we observe a clear correlation between the sound sources that are present in the urban soundscape and its induced emotions, specially regarding the sensation of pleasantness. Models like the ones proposed in this paper allow for an assessment of the acoustic environment that goes beyond a characterisation solely based on sound pressure level measurements and could be integrated into current acoustic monitoring solutions to enhance the understanding from the perspective of the induced emotions.

Benno Weck and Frederic Font. "The Language of Sound Search: Examining User Queries in Audio Search Engines". 

This study examines textual, user-written search queries within the context of sound search engines, encompassing various applications such as foley, sound effects, and general audio retrieval. Current research inadequately addresses real-world user needs and behaviours in designing text-based audio retrieval systems. To bridge this gap, we analysed search queries from two sources: a custom survey and Freesound website query logs. The survey was designed to collect queries for an unrestricted, hypothetical sound search engine, resulting in a dataset that captures user intentions without the constraints of existing systems. This dataset is also made available for sharing with the research community. In contrast, the Freesound query logs encompass approximately 9 million search requests, providing a comprehensive view of real-world usage patterns. Our findings indicate that survey queries are generally longer than Freesound queries, suggesting users prefer detailed queries when not limited by system constraints. Both datasets predominantly feature keyword-based queries, with few survey participants using full sentences. Key factors influencing survey queries include the primary sound source, intended usage, perceived location, and the number of sound sources. These insights are crucial for developing user-centred, effective text-based audio retrieval systems, enhancing our understanding of user behaviour in sound search contexts.

The full proceedings of the workshop are available online https://dcase.community/workshop2024/proceedings.