Instrument Recognition
20 papers with code • 3 benchmarks • 4 datasets
NSynth
Kvasir-Instrument
YouTube-100M
OpenMIC-2018
Most implemented papers
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development.
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks
As state-of-the-art CNN architectures-in computer vision and other domains-tend to go deeper in terms of number of layers, their RF size increases and therefore they degrade in performance in several audio classification and tagging tasks.
Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition
hugofloresgarcia/music-trees
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Deep learning work on musical instrument recognition has generally focused on instrument classes for which we have abundant data.
Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds
Alexuan/musical_instrument_embedding
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Constructing an embedding space for musical instrument sounds that can meaningfully represent new and unseen instruments is important for downstream music generation tasks such as multi-instrument synthesis and timbre transfer.
ChMusic: A Traditional Chinese Music Dataset for Evaluation of Instrument Recognition
This paper propose a traditional Chinese music dataset for training model and performance evaluation, named ChMusic.
EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use
In audio classification, differentiable auditory filterbanks with few parameters cover the middle ground between hard-coded spectrograms and raw audio.
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
It focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets.
Audio Embeddings as Teachers for Music Classification
suncerock/EAsT-music-classification
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Music classification has been one of the most popular tasks in the field of music information retrieval.
Transfer Learning and Bias Correction with Pre-trained Audio Embeddings
changhongw/audio-embedding-bias
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This approach allows representations derived for one task to be applied to another, and can result in high accuracy with less stringent training data requirements for the downstream task.
Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models
Audio Spectrogram Transformers are excellent at exploiting large datasets, creating powerful pre-trained models that surpass CNNs when fine-tuned on downstream tasks.
