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Showing 1 to 18 of 18 for “"Audio classification"”.

  1. Data-efficient approaches for audio classification and separation

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-03-01 without embargo terms

    uiuc Repository record for Data-efficient approaches for audio classification and separation (opens in a new tab)

  2. Investigating audio classification to automate the trimming of recorded lectures

    … In this study, we investigate the potential of audio classification to automate this step. A classification model was trained to detect 2 classes: speech and non-speech. Speech represents a single dominant voice, for example, the lecturer, and non-speech represents student chatter, silence and …

    cape-town Repository record for Investigating audio classification to automate the trimming of recorded lectures (opens in a new tab)

  3. Optimal feature selection and machine learning for high-level audio classification : a random forests approach

    … text mining process to obtain semantics for the audio scene. An efficient speech, music and environmental sound classification system, which correctly identify these three types of audio signals and feed them into dedicated recognisers, is a critical pre-processing stage for such a content …

    salford Repository record for Optimal feature selection and machine learning for high-level audio classification : a random forests approach (opens in a new tab)

  4. Overlapped speech and music segmentation using singular spectrum analysis and random forests

    … broadcasting, and semantic web have emerged, audio information mining and automated metadata generation have received much attention. Manual indexing and metadata tagging are time-consuming and subject to the biases of individual workers. An automated architecture able to extract information …

    salford Repository record for Overlapped speech and music segmentation using singular spectrum analysis and random forests (opens in a new tab)

  5. Efficient Knowledge Transfer and Adaptation for Speech and Beyond

    … of transfer learning in dynamically evolving audio and speech processing contexts, particularly through novel approaches for class-incremental learning, parameter-efficient adaptation, and multimodal modeling. First, we provide a comprehensive framework for class-incremental spoken language …

    trento Repository record for Efficient Knowledge Transfer and Adaptation for Speech and Beyond (opens in a new tab)

  6. Resource management in sensing services with audio applications

    … high data-rate sensing modalities such as audio/video. This work therefore investigates the resource management problem in sensing services, with application in audio sensing. First, a modular, data-centric architecture is proposed as the framework within which optimal resource management …

    uiuc Repository record for Resource management in sensing services with audio applications (opens in a new tab)

  7. SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning

    … to evaluate the SigSpace model in image classification using large scale image datasets including Caltech-101, Caltech-256, ImageNet, UEC FOOD 256, MNIST with image features like Pixels, SIFT, and Local Binary Pattern. Secondly, SigSpace was evaluated in the audio classification context …

    umkc Repository record for SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning (opens in a new tab)

  8. Bioacoustic classification of Hainan gibbon call types using deep learning

    … successfully been used in computer vision and audio classification tasks. This study is the first attempt at investigating how deep learning can be used to distinguish between the Hainan gibbon social groups using only the acoustic data recorded in BNNR. Two convolutional neural networks (CNNs) …

    cape-town Repository record for Bioacoustic classification of Hainan gibbon call types using deep learning (opens in a new tab)

  9. Listening by Synthesizing

    Generative audio models offer a scalable solution for producing a rich variety of sounds. This can be useful for practical tasks, like sound design in music, film, and other media. However, these models overwhelmingly rely on deep neural networks, and their massive complexity hinders our ability to …

    mit Repository record for Listening by Synthesizing (opens in a new tab)

  10. Using conditional restricted Boltzmann machines to generate timbral music composition systems

    … composition in a variety of forms, including audio classification, recognition, and synthesis. The capability of algorithms to learn complex musical elements allows composers to more deeply investigate the development of their aesthetic. Coupled with the history of interdisciplinary solutions …

    uiuc Repository record for Using conditional restricted Boltzmann machines to generate timbral music composition systems (opens in a new tab)

  11. Embedded Real-time Deep Learning for a Smart Guitar: A Case Study on Expressive Guitar Technique Recognition

    … computers become more capable and new embedded audio platforms are developed, new avenues for real-time embedded gesture acquisition open up. Expressive guitar technique recognition is the task of detecting notes and classifying the playing techniques used by the musician on the instrument. …

    trento Repository record for Embedded Real-time Deep Learning for a Smart Guitar: A Case Study on Expressive Guitar Technique Recognition (opens in a new tab)

  12. Learning with Limited Labeled Data: Techniques and Applications

    … as image generation, question answering, and audio classification. However, these deep and high-capacity models require a large amount of labeled data to function properly, rendering them inapplicable in many real-world scenarios. This dissertation focuses on the development and evaluation of …

    vt Repository record for Learning with Limited Labeled Data: Techniques and Applications (opens in a new tab)

  13. Graph-Based Acoustic Clustering and Classification

    <p>The rapid growth of audio data collection in various domains necessitates advanced techniquesfor efficient analysis and classification. This dissertation proposes new approaches for categorizing acoustic data, using both unsupervised and semi-supervised learning methods. Starting with raw audio, …

    claremont Repository record for Graph-Based Acoustic Clustering and Classification (opens in a new tab)

  14. Analysing and Mitigating Classification Bias for Text-based Foundation Models

    The objective of text classification is to categorise texts into one of several pre-defined classes. Text classification is a standard natural language processing (NLP) task with various applicability in many domains, such as analysing the evolving sentiment of users on a platform, identifying and …

    cambridge Repository record for Analysing and Mitigating Classification Bias for Text-based Foundation Models (opens in a new tab)

  15. Understanding modern deep learning techniques for audio applications and beyond

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms

    uiuc Repository record for Understanding modern deep learning techniques for audio applications and beyond (opens in a new tab)

  16. Speech Foundation Models for Audio Processing

    … shown strong performance across a variety of audio processing tasks, including automatic speech recognition (ASR) and speech translation. Unlike traditional systems that require task-specific architectures and extensive supervision, these models offer a unified and flexible framework that …

    cambridge Repository record for Speech Foundation Models for Audio Processing (opens in a new tab)

  17. Automatic classification of electronic music and speech/music audio content

    Automatic audio categorization has great potential for application in the maintenance and usage of large and constantly growing media databases; accordingly, much research has been done to demonstrate the feasibility of such methods. A popular topic is that of automatic genre classification, …

    uiuc Repository record for Automatic classification of electronic music and speech/music audio content (opens in a new tab)