Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study | Paper Citation Record

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This paper cites PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition,

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This paper cites Embeddings for up to 2000 random samples from the validation partition of select datasets representing speech, non-speech and VAD audio domains.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Embeddings for up to 2000 random samples from the validation partition of select datasets representing speech, non-speech and VAD audio domains

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This paper cites Using State of the Art Speaker Recognition and Natural Language Processing Technologies to Detect Alzheimer’s Disease and Assess its Severity,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Using State of the Art Speaker Recognition and Natural Language Processing Technologies to Detect Alzheimer’s Disease and Assess its Severity,

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This paper cites wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations,

Reference 4

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This paper cites Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set,

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This paper cites Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests,

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This paper cites Using X-Vectors to Automatically Detect Parkinson’s Disease from Speech,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Using X-Vectors to Automatically Detect Parkinson’s Disease from Speech,

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This paper cites Representation Learning with Contrastive Predictive Coding.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Representation Learning with Contrastive Predictive Coding

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This paper cites Unsupervised Cross-lingual Representation Learning for Speech Recognition.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Unsupervised Cross-lingual Representation Learning for Speech Recognition

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

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This paper cites BYOL for Audio: Exploring Pre-Trained General-Purpose Audio Representations,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study BYOL for Audio: Exploring Pre-Trained General-Purpose Audio Representations,

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This paper cites BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation

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This paper cites WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing,

Reference 13

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This paper cites The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines

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This paper cites Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Unleashing the killer corpus: experiences in creating the multi-everything AMI Meeting Corpus,

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This paper cites Common Voice: A Massively-Multilingual Speech Corpus,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Common Voice: A Massively-Multilingual Speech Corpus,

Reference 16

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This paper cites Available: https://aclanthology.org/2020.lrec-1.520.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Available: https://aclanthology.org/2020.lrec-1.520

Reference 17

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This paper cites Recognition and understanding of meetings the AMI and AMIDA projects,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Recognition and understanding of meetings the AMI and AMIDA projects,

Reference 18

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This paper cites Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks,

Reference 19

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This paper cites VoxCeleb: A Large-Scale Speaker Identification Dataset,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study VoxCeleb: A Large-Scale Speaker Identification Dataset,

Reference 20

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Librispeech: An ASR corpus based on public domain audio books,

Reference 21

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This paper cites General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Audio tagging with noisy labels and minimal supervision

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study The Multilingual TEDx Corpus for Speech Recognition and Translation

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study MUSAN: A Music, Speech, and Noise Corpus

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study An open dataset for research on audio field recording archives: freefield1010

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study FSD50K: An Open Dataset of Human-Labeled Sound Events,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Unresolved cited work

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Audio Set: An ontology and human-labeled dataset for audio events,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study CNN architectures for large-scale audio classification,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study WHAM!: Extending Speech Separation to Noisy Environments

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Bootstrap your own latent a new approach to self-supervised learning,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Environmental sound classification with convolutional neural networks,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study A Dataset and Taxonomy for Urban Sound Research,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders,

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This paper cites CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit (version 0.92),.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit (version 0.92),

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Representational geometry: integrating cognition, computation, and the brain,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study The Timbre Toolbox: Extracting audio descriptors from musical signals,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study The Modulation Transfer Function for Speech Intelligibility,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study YIN, a fundamental frequency estimator for speech and music,

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This paper cites Acoustic Event Detection Using Speaker Recognition Techniques: Model Optimization and Explainable Features,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Acoustic Event Detection Using Speaker Recognition Techniques: Model Optimization and Explainable Features,

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This paper cites Acoustic Correlates of Auditory Object and Event Perception: Speakers, Musical Timbres, and Environmental Sounds,.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Acoustic Correlates of Auditory Object and Event Perception: Speakers, Musical Timbres, and Environmental Sounds,

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Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study AVES: Animal Vocalization Encoder based on Self-Supervision

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This paper cites SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network

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This paper cites Available: https://proceedings.neurips.cc/paper/2020/hash/92d1e1eb1cd6f9fba3227870bb6d7f07-Abstract.html.

Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study Available: https://proceedings.neurips.cc/paper/2020/hash/92d1e1eb1cd6f9fba3227870bb6d7f07-Abstract.html

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