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Audio Spectrogram Representations for Processing with Convolutional Neural Networks

arXiv 1706.09559 ↗First posted 29 Jun 2017cs.SD cs.LG cs.MMcited by 15

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Abstract (arXiv)

One of the decisions that arise when designing a neural network for any application is how the data should be represented in order to be presented to, and possibly generated by, a neural network. For audio, the choice is less obvious than it seems to be for visual images, and a variety of representations have been used for different applications including the raw digitized sample stream, hand-crafted features, machine discovered features, MFCCs and variants that include deltas, and a variety of spectral representations. This paper reviews some of these representations and issues that arise, focusing particularly on spectrograms for generating audio using neural networks for style transfer.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2017.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 0; 2024: 3; 2025: 2; 2026: 1.

15 papers.

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  1. 2024animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacousticscited by 14
  2. 2022Multi-modal Fusion Technology based on Vehicle Information: A Surveycited by 13
  3. 2021When are Deep Networks really better than Decision Forests at small sample sizes, and how?cited by 6
  4. 2021Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devicescited by 5
  5. 2021Leveraging Image-based Generative Adversarial Networks for Time Series Generationcited by 4
  6. 2022HarmoF0: Logarithmic Scale Dilated Convolution For Pitch Estimationcited by 4
  7. 2021Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicescited by 3
  8. 2022Urban Rhapsody: Large-scale exploration of urban soundscapescited by 3
  9. 2021AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queriescited by 2
  10. 2024Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturingcited by 2
  11. 2025DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillationcited by 2
  12. 2025A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do~nanacited by 1
3 more
  1. 2022Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plotscited by 0
  2. 2024Tuning In: Analysis of Audio Classifier Performance in Clinical Settings with Limited Datacited by 0
  3. 2026SpecRegMatch: Robust Semi-Supervised Regression for Vehicle Interior Noise Predictioncited by 0