Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 18 of 18 for “"Audio classification"”.
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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
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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 …
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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 …
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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 …
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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 …
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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 …
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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 …
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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) …
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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 …
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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 …
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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. …
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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 …
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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, …
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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 …
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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
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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 …
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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, …