Instrument Recognition
20 papers with code • 3 benchmarks • 4 datasets
NSynth
Kvasir-Instrument
YouTube-100M
OpenMIC-2018
Most implemented papers
ATST: Audio Representation Learning with Teacher-Student Transformer
Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data.
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices.
Efficient Training of Audio Transformers with Patchout
kkoutini/passt
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However, one of the main shortcomings of transformer models, compared to the well-established CNNs, is the computational complexity.
Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
In order to tackle both clip-level and frame-level tasks, this paper proposes Audio Teacher-Student Transformer (ATST), with a clip-level version (named ATST-Clip) and a frame-level version (named ATST-Frame), responsible for learning clip-level and frame-level representations, respectively.
Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks
Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm.
Deep convolutional neural networks for predominant instrument recognition in polyphonic music
We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length.
Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
CAMMA-public/ConvLSTM-Surgical-Tool-Tracker
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Results: We build a baseline tracker on top of the CNN model and demonstrate that our approach based on the ConvLSTM outperforms the baseline in tool presence detection, spatial localization, and motion tracking by over 5. 0%, 13. 9%, and 12. 6%, respectively.
An Attention Mechanism for Musical Instrument Recognition
While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings.
Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music
Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest.
Predominant Musical Instrument Classification based on Spectral Features
This work aims to examine one of the cornerstone problems of Musical Instrument Retrieval (MIR), in particular, instrument classification.
