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Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music
TLDR
The analysis on the instrument-wise performance found that the onset type is a critical factor for recall and precision of each instrument, and convolutional neural networks are more robust than conventional methods that exploit spectral features and source separation with support vector machines.
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Phase-aware Speech Enhancement with Deep Complex U-Net
TLDR
A novel loss function, weighted source-to-distortion ratio (wSDR) loss, which is designed to directly correlate with a quantitative evaluation measure and achieves state-of-the-art performance in all metrics.
View PDF on arXiv
Rare Sound Event Detection Using 1D Convolutional Recurrent Neural Networks
TLDR
The proposed system using combination of 1D convolutional neural network and recurrent neural network (RNN) with long shortterm memory units (LSTM) has achieved the 1st place in the challenge with an error rate of 0.13 and an F-Score of 93.1.
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Convolutional Neural Networks with Binaural Representations and Background Subtraction for Acoustic Scene Classification
TLDR
The experimental results show that the proposed network structures and the preprocessing methods effectively learn acoustic characteristics from the audio recordings, and their ensemble model significantly reduces the error rate further.
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Acoustic Chord Transcription and Key Extraction From Audio Using Key-Dependent HMMs Trained on Synthesized Audio
  • Kyogu Lee, M. Slaney
  • Computer Science
    IEEE Transactions on Audio, Speech, and Language…
  • 1 February 2008
TLDR
An acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results and the robustness of the tonal centroid feature, which outperforms the conventional chroma feature.
View on IEEE
Adversarially Trained End-to-end Korean Singing Voice Synthesis System
TLDR
It is shown that two other proposed methods -- local conditioning of text and pitch, and conditional adversarial training -- are crucial for a realistic generation of the human singing voice in the super-resolution process.
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Automatic Chord Recognition from Audio Using Enhanced Pitch Class Profile
TLDR
A feature vector called the Enhanced Pitch Class Profile (EPCP) is introduced for automatic chord recognition from the raw audio, which yields less errors than the conventional PCP in frame-rate chord recognition.
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ENSEMBLE OF CONVOLUTIONAL NEURAL NETWORKS FOR WEAKLY-SUPERVISED SOUND EVENT DETECTION USING MULTIPLE SCALE INPUT
TLDR
The proposed model, an ensemble of convolutional neural networks to detect audio events in the automotive environment, achieved the 2nd place on audio tagging and the 1st place on sound event detection.
Music Source Separation Using Stacked Hourglass Networks
TLDR
Experimental results on MIR-1K and DSD100 datasets validate that the proposed method achieves competitive results comparable to the state-of-the-art methods in multiple music source separation and singing voice separation tasks.
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#nowplaying the future billboard: mining music listening behaviors of twitter users for hit song prediction
TLDR
The results show that the numbers of daily tweets about a specific song and artist can be effectively used to predict Billboard rankings and hits and could play an important role in understanding consumers' music consumption patterns.
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