18 accepted papers
This work presents advancements in audio pretraining objectives designed to generate semantically rich embeddings, capable of addressing a wide range of audio-related tasks. Despite significant progress in the field, current methods often emphasize full fine-tuning in downstream applications, which…
Autoregressive next-token prediction with the Transformer decoder has become a de facto standard in large language models (LLMs), achieving remarkable success in Natural Language Processing (NLP) at scale. Extending this paradigm to audio poses unique challenges due to its inherently continuous natu…
Text-to-audio generation synthesizes realistic sounds or music given a natural language prompt. Diffusion-based frameworks, including the Tango and the AudioLDM series, represent the state-of-the-art in text-to-audio generation. Despite achieving high audio fidelity, they incur significant inference…
Cross-triggering is a critical problem for applications of audio event detection (AED), particularly in low-resource settings. However, not much attention (if not none) has been paid to this problem in the AED research community. In this work, we tackle this problem via a regularization approach. We…
In deep metric learning for visual recognition the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR). However calibrating this thresh- old presents challenges in open-world scenarios where the test clas…
Large self-supervised models have excelled in various speech processing tasks, but their deployment on resource-limited devices is often impractical due to their substantial memory footprint. Previous studies have demonstrated the effectiveness of self-supervised pre-training for keyword spotting, e…
Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes and data distributions. When combined with the common practice of using a fixed threshold to declare a match, this gives r…
Performance and robustness of real-world Acoustic Event Classification (AEC) solutions depend on ability to train on diverse data from wide range of end-point devices and acoustic environments. Federated Learning (FL) provides a framework to leverage annotated and non-annotated AEC data from servers…
Acoustic Event Classification (AEC) has been widely used in devices such as smart speakers and mobile phones for home safety or accessibility support [1]. As AEC models run on more and more devices with diverse computation resource constraints, it became increasingly expensive to develop models that…
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling frame- work that decouples data collection and model training to enhance customer privacy. In this work, we investigate th…
Acoustic events have a hierarchical structure analogous to a tree (or a directed acyclic graph). In this work, we propose a structure-aware semi-supervised learning framework for acoustic event classification (AEC). Our hypothesis is that the audio label structure contains useful information that is…
Acoustic event classification (AEC) is the task of determining whether certain events occur in an audio clip. Inspired by previous research [1], [2], [3] that embeddings from event labels can be leveraged to facilitate the learning of new detectors with no or limited audio samples, we introduce Wiki…
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data problem, as human annotations are costly to acquire, and annotations for audio are time consuming and less intuitive. B…
We propose an approach for pre-training speech representations via a masked reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be used directly in typical bidirectional speech recognition models. The pre-trained networks can then be fine-tuned on a smaller amou…
We study media presence detection, that is, learning to recognize if a sound segment (typically lasting for a few seconds) of a long recorded stream contains media (TV) sound. This problem is difficult because non-media sound sources can be quite diverse (e.g. human voicing, non-vocal sounds and non…
Previous work has shown that it is possible to improve speech recognition by learning acoustic features from paired acoustic-articulatory data, for example by using canonical correlation analysis (CCA) or its deep extensions. One limitation of this prior work is that the learned feature models are d…
This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogeneous contents containing orderless images and videos. The importance of each image is usually considered either equal or…
Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network infer…