Audio Classification
131 papers with code • 23 benchmarks • 34 datasets
Audio Classification is a machine learning task that involves identifying and tagging audio signals into different classes or categories. The goal of audio classification is to enable machines to automatically recognize and distinguish between different types of audio, such as music, speech, and environmental sounds.
Benchmarks
These leaderboards are used to track progress in Audio Classification
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Libraries
Use these libraries to find Audio Classification models and implementationsDatasets
AudioSet
Speech Commands
ESC-50
VGG-Sound
EPIC-KITCHENS-100
UrbanSound8K
FSD50K
UCR Time Series Classification Archive
Subtasks
Latest papers with no code
Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities
We propose a multimodal model, called Mirasol3B, consisting of an autoregressive component for the time-synchronized modalities (audio and video), and an autoregressive component for the context modalities which are not necessarily aligned in time but are still sequential.
OmniVec: Learning robust representations with cross modal sharing
We demonstrate empirically that, using a joint network to train across modalities leads to meaningful information sharing and this allows us to achieve state-of-the-art results on most of the benchmarks.
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks
In this study, we focus on improving the poisoning efficiency of backdoor attacks from the sample selection perspective.
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
In this paper, we propose CompA, a collection of two expert-annotated benchmarks with a majority of real-world audio samples, to evaluate compositional reasoning in ALMs.
Diffusion Models as Masked Audio-Video Learners
Over the past several years, the synchronization between audio and visual signals has been leveraged to learn richer audio-visual representations.
Audio Contrastive based Fine-tuning
Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications.
Improving Speech Recognition for African American English With Audio Classification
By combining the classifier output with coarse geographic information, we can select a subset of utterances from a large corpus of untranscribed short-form queries for semi-supervised learning at scale.
Exploring Meta Information for Audio-based Zero-shot Bird Classification
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research.
Diverse Neural Audio Embeddings -- Bringing Features back !
With the advent of modern AI architectures, a shift has happened towards end-to-end architectures.
Learning Speech Representation From Contrastive Token-Acoustic Pretraining
However, existing contrastive learning methods in the audio field focus on extracting global descriptive information for downstream audio classification tasks, making them unsuitable for TTS, VC, and ASR tasks.
