# Deep convolutional networks on the pitch spiral for musical instrument recognition

arXiv: https://arxiv.org/abs/1605.06644 · HTML: https://mlsanity.com/paper/1605.06644/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2016.

## Built on by (most cited first, 15 of 15)

- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [Benchmarking Representations for Speech, Music, and Acoustic Events](https://mlsanity.com/paper/2405.00934/) (2024)
- [Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition](https://mlsanity.com/paper/2107.07029/) (2021)
- [Music Enhancement via Image Translation and Vocoding](https://mlsanity.com/paper/2204.13289/) (2022)
- [A2SB: Audio-to-Audio Schrodinger Bridges](https://mlsanity.com/paper/2501.11311/) (2025)
- [Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data](https://mlsanity.com/paper/2410.02056/) (2024)
- [Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio](https://mlsanity.com/paper/2402.09318/) (2024)
- [Show Me the Instruments: Musical Instrument Retrieval from Mixture Audio](https://mlsanity.com/paper/2211.07951/) (2022)
- [Music Instrument Classification Reprogrammed](https://mlsanity.com/paper/2211.08379/) (2022)
- [Differentiable Time-Frequency Scattering on GPU](https://mlsanity.com/paper/2204.08269/) (2022)
- [Introducing topography in convolutional neural networks](https://mlsanity.com/paper/2211.13152/) (2022)
- [Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data](https://mlsanity.com/paper/2307.12576/) (2023)
- [Audio Contrastive-based Fine-tuning: Decoupling Representation Learning and Classification](https://mlsanity.com/paper/2309.11895/) (2023)
- [Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training](https://mlsanity.com/paper/2506.17499/) (2025)
- [What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities](https://mlsanity.com/paper/2610.19476/) (2026)

## Abstract (arXiv)

Musical performance combines a wide range of pitches, nuances, and expressive techniques. Audio-based classification of musical instruments thus requires to build signal representations that are invariant to such transformations. This article investigates the construction of learned convolutional architectures for instrument recognition, given a limited amount of annotated training data. In this context, we benchmark three different weight sharing strategies for deep convolutional networks in the time-frequency domain: temporal kernels; time-frequency kernels; and a linear combination of time-frequency kernels which are one octave apart, akin to a Shepard pitch spiral. We provide an acoustical interpretation of these strategies within the source-filter framework of quasi-harmonic sounds with a fixed spectral envelope, which are archetypal of musical notes. The best classification accuracy is obtained by hybridizing all three convolutional layers into a single deep learning architecture.