Neural Pitch Encoder Training With Frequency-Shifted Audio
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Solution Overview
Problem
Existing pitch detection methods struggle with accuracy in noisy environments and require extensive annotated data for supervised learning, especially for high temporal and frequency resolution.
Innovation Solution
A self-supervised technique for training a neural network encoder to predict pitch using frequency-shifted audio samples, allowing for relative pitch estimation and calibration to determine absolute pitch values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning techniques are used to train an encoder for pitch prediction, then pitch prediction accuracy can be improved, but the cost and difficulty of generating sufficient annotated training data increases significantly
Solution Approach 1:
The system uses self-supervised learning where the model generates its own training signals by predicting pitch from audio samples and using those predictions to create frequency-shifted training samples. This eliminates the need for extensive manually annotated data while maintaining high pitch prediction accuracy through autonomous data generation and model refinement.
2Measurement precision
If high temporal and frequency resolution is desired for pitch detection, then pitch prediction accuracy improves, but the requirement for annotated training data increases
Solution Approach 1:
The system performs preliminary pitch predictions on audio samples before formal training begins. These preliminary predictions are then used to generate frequency-shifted training samples that are tailored to the specific characteristics of the audio data, enabling high-resolution pitch detection without requiring pre-prepared annotated datasets at the desired resolution.
3Ease of operation
If heuristic algorithms are used for pitch detection, then the method is simple to implement, but performance deteriorates in the presence of noise or multiple pitch sources
Solution Approach 1:
The system replaces traditional heuristic algorithms with a neural network-based encoder that learns pitch detection patterns directly from data. This substitution enables the model to handle noisy environments and multiple pitch sources effectively while maintaining implementation simplicity through automated training and inference processes.
Data Source
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AI summary
Example embodiments relate to techniques for training artificial neural networks or oilier machine-learning encoders to accurately predict the pitch of input audio samples in a semitone or otherwise logarithmically-scaled pitch space. An example method may include generating, from a sample of audio data, two training samples by applying two di fferent pitch shifts to the sample of audio training data. This can be done by converting the sample of audio data into the frequency domain and then shifting the transformed data. These known shifts are then compared to the predicted pitches generated by applying the two training samples to the encoder. The encoder is then updated based on the comparison, such that the relative pitch output by the encoder is improved with respect to accuracy. One or more audio samples, labeled with absolute pitch values, can then be used to calibrate the relative pitch values generated by the trained encoder.