# Music Augmentation and Denoising For Peak-Based Audio Fingerprinting

arXiv: https://arxiv.org/abs/2310.13388 · HTML: https://mlsanity.com/paper/2310.13388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed](https://mlsanity.com/paper/1909.01174/) (2019)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [CMGAN: Conformer-based Metric GAN for Speech Enhancement](https://mlsanity.com/paper/2203.15149/) (2022)
- [TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain](https://mlsanity.com/paper/2103.09963/) (2021)
- [DPT-FSNet: Dual-path Transformer Based Full-band and Sub-band Fusion Network for Speech Enhancement](https://mlsanity.com/paper/2104.13002/) (2021)
- [MANNER: Multi-view Attention Network for Noise Erasure](https://mlsanity.com/paper/2203.02181/) (2022)
- [Improved Speech Enhancement with the Wave-U-Net](https://mlsanity.com/paper/1811.11307/) (2018)
- [Dual-branch Attention-In-Attention Transformer for single-channel speech enhancement](https://mlsanity.com/paper/2110.06467/) (2021)
- [A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings](https://mlsanity.com/paper/2202.08702/) (2022)
- [Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning](https://mlsanity.com/paper/2010.11910/) (2020)
- [Music Enhancement via Image Translation and Vocoding](https://mlsanity.com/paper/2204.13289/) (2022)
- [Now Playing: Continuous low-power music recognition](https://mlsanity.com/paper/1711.10958/) (2017)
- [Learning to Denoise Historical Music](https://mlsanity.com/paper/2008.02027/) (2020)
- [Attention-Based Audio Embeddings for Query-by-Example](https://mlsanity.com/paper/2210.08624/) (2022)
- [MicAugment: One-shot Microphone Style Transfer](https://mlsanity.com/paper/2010.09658/) (2020)
- [Contrastive Unsupervised Learning for Audio Fingerprinting](https://mlsanity.com/paper/2010.13540/) (2020)
- [Audio Denoising for Robust Audio Fingerprinting](https://mlsanity.com/paper/2212.11277/) (2022)

## Abstract (arXiv)

Audio fingerprinting is a well-established solution for song identification from short recording excerpts. Popular methods rely on the extraction of sparse representations, generally spectral peaks, and have proven to be accurate, fast, and scalable to large collections. However, real-world applications of audio identification often happen in noisy environments, which can cause these systems to fail. In this work, we tackle this problem by introducing and releasing a new audio augmentation pipeline that adds noise to music snippets in a realistic way, by stochastically mimicking real-world scenarios. We then propose and release a deep learning model that removes noisy components from spectrograms in order to improve peak-based fingerprinting systems' accuracy. We show that the addition of our model improves the identification performance of commonly used audio fingerprinting systems, even under noisy conditions.