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Found 9 papers, 4 papers with code
Timbre-Trap: A Low-Resource Framework for Instrument-Agnostic Music Transcription
Several works have explored multi-instrument transcription as a means to bolster the performance of models on low-resource tasks, but these methods face the same data availability issues.
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training
Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outputs instrument-specific piano rolls, and a source separation module that utilizes instrument information and transcription results.
DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability
In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT).
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications
However, its novelty necessitates a new perspective on how to evaluate such a model.
ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data
Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize.
The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy
We attempt to use only the pitch labels (together with spectrogram reconstruction loss) and explore how far this model can go without introducing supervised sub-tasks.
The impact of Audio input representations on neural network based music transcription
This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription.
Sound Audio and Speech Processing
nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks
First, it takes a lot of hard disk space to store different frequency domain representations.
Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks
When reducing the training data to only using the train set, our method results in 309 confusions for the Multi-target speaker identification task, which is 46% better than the baseline model.
