Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Audio-Visual Contrastive Learning with Temporal Self-Supervision
We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and the accompanying audio without human supervision. In contrast to images that capture the static scene appea…
Action RecognitionAudio ClassificationContrastive LearningRetrieval+1SemanticAC: Semantics-Assisted Framework for Audio Classification
In this paper, we propose SemanticAC, a semantics-assisted framework for Audio Classification to better leverage the semantic information. Unlike conventional audio classification methods that treat class labels as discr…
Audio ClassificationClassificationLanguage ModelingLanguage ModellingMultimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models
The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality…
Audio ClassificationFew-Shot LearningVisual Transformers for Primates Classification and Covid Detection
We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and samp…
Audio ClassificationClassificationData AugmentationBEATs: Audio Pre-Training with Acoustic Tokenizers
The massive growth of self-supervised learning (SSL) has been witnessed in language, vision, speech, and audio domains over the past few years. While discrete label prediction is widely adopted for other modalities, the …
Audio ClassificationSelf-Supervised LearningAudiovisual Masked Autoencoders
Can we leverage the audiovisual information already present in video to improve self-supervised representation learning? To answer this question, we study various pretraining architectures and objectives within the maske…
Audio ClassificationRepresentation LearningPex: Memory-efficient Microcontroller Deep Learning through Partial Execution
Embedded and IoT devices, largely powered by microcontroller units (MCUs), could be made more intelligent by leveraging on-device deep learning. One of the main challenges of neural network inference on an MCU is the ext…
Audio ClassificationDeep LearningA Spatio-temporal Deep Learning Approach for Underwater Acoustic Signals Classification
Target recognition from underwater acoustic signals is a major challenge in surveillance systems, especially in military and defense fields. Deep learning models are increasingly used for the automatic classification of …
Audio ClassificationClassificationSpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms
Pattern recognition from audio signals is an active research topic encompassing audio tagging, acoustic scene classification, music classification, and other areas. Spectrogram and mel-frequency cepstral coefficients (MF…
Acoustic Scene ClassificationAnomaly DetectionAudio ClassificationAudio Tagging+4Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale d…
Audio ClassificationAudio TaggingComputational EfficiencyKnowledge Distillation"Seeing Sound": Audio Classification with the Wigner-Wille Distribution and Convolutional Neural Networks
With big data becoming increasingly available, IoT hardware becoming widely adopted, and AI capabilities becoming more powerful, organizations are continuously investing in sensing. Data coming from sensor networks are c…
Audio ClassificationSelf-Driving CarsSensor FusionEffective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block
Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiv…
Audio ClassificationClassificationData AugmentationEnvironmental Sound Classification+3SLICER: Learning universal audio representations using low-resource self-supervised pre-training
We present a new Self-Supervised Learning (SSL) approach to pre-train encoders on unlabeled audio data that reduces the need for large amounts of labeled data for audio and speech classification. Our primary aim is to le…
Audio ClassificationClusteringContrastive LearningRepresentation Learning+2MAST: Multiscale Audio Spectrogram Transformers
We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST). Given an input audio spectrogram…
Audio ClassificationKeyword SpottingSelf-Supervised LearningPretraining Respiratory Sound Representations using Metadata and Contrastive Learning
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in t…
Audio ClassificationClassificationContrastive LearningRepresentation Learning+1Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input
Masked Autoencoders is a simple yet powerful self-supervised learning method. However, it learns representations indirectly by reconstructing masked input patches. Several methods learn representations directly by predic…
Audio ClassificationAudio TaggingKeyword SpottingKeyword Spotting on Google Speech Commands+3Adaptive re-calibration of channel-wise features for Adversarial Audio Classification
DeepFake Audio, unlike DeepFake images and videos, has been relatively less explored from detection perspective, and the solutions which exist for the synthetic speech classification either use complex networks or dont g…
Audio ClassificationFace SwappingSynthetic Speech Detectiontext-to-speech+1Play It Back: Iterative Attention for Audio Recognition
A key function of auditory cognition is the association of characteristic sounds with their corresponding semantics over time. Humans attempting to discriminate between fine-grained audio categories, often replay the sam…
Audio ClassificationSTSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks
Spiking Neural Networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and h…
Audio ClassificationGesture Recognitionimage-classificationImage ClassificationLearning Temporal Resolution in Spectrogram for Audio Classification
The audio spectrogram is a time-frequency representation that has been widely used for audio classification. One of the key attributes of the audio spectrogram is the temporal resolution, which depends on the hop size us…
Audio ClassificationGeneral Classification