Environmental Sound Classification
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Benchmarks
Most implemented
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
AudioCLIP: Extending CLIP to Image, Text and Audio
Rethinking CNN Models for Audio Classification
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit
CRNNs for Urban Sound Tagging with spatiotemporal context
Papers
MAEB: Massive Audio Embedding Benchmark
We introduce the Massive Audio Embedding Benchmark (MAEB), a large-scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross-modal audio-text reasoning in 100+ languages. We evaluate 50+ mod…
Environmental Sound ClassificationExpressive Range Characterization of Open Text-to-Audio Models
Text-to-audio models are a type of generative model that produces audio output in response to a given textual prompt. Although level generators and the properties of the functional content that they create (e.g., playabi…
Environmental Sound ClassificationCompressing Quaternion Convolutional Neural Networks for Audio Classification
Conventional Convolutional Neural Networks (CNNs) in the real domain have been widely used for audio classification. However, their convolution operations process multi-channel inputs independently, limiting the ability …
Environmental Sound ClassificationSpeech Emotion RecognitionMusic Genre RecognitionKnowledge DistillationASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning
In recent advancements in audio self-supervised representation learning, the standard Transformer architecture has emerged as the predominant approach, yet its attention mechanism often allocates a portion of attention w…
Environmental Sound ClassificationRepresentation LearningAudio ClassificationKeyword SpottingDomain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Audio-text models are widely used in zero-shot environmental sound classification as they alleviate the need for annotated data. However, we show that their performance severely drops in the presence of background sound …
ClassificationDomain AdaptationEnvironmental Sound ClassificationSound ClassificationWeakly Supervised Convolutional Dictionary Learning for Multi-Label Classification
Convolutional Dictionary Learning (CDL) has emerged as a powerful approach for signal representation by learning translation-invariant features through convolution operations. While existing CDL methods are predominantly…
ClassificationDictionary LearningEnvironmental Sound ClassificationMulti-Label Classification+2