# Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming

arXiv: https://arxiv.org/abs/2211.01317 · HTML: https://mlsanity.com/paper/2211.01317/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021)
- [Adversarial Reprogramming of Neural Networks](https://mlsanity.com/paper/1806.11146/) (2018)
- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://mlsanity.com/paper/2106.09296/) (2021)
- [Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources](https://mlsanity.com/paper/2007.08714/) (2020)
- [Codified audio language modeling learns useful representations for music information retrieval](https://mlsanity.com/paper/2107.05677/) (2021)
- [musicnn: Pre-trained convolutional neural networks for music audio tagging](https://mlsanity.com/paper/1909.06654/) (2019)
- [A neural attention model for speech command recognition](https://mlsanity.com/paper/1808.08929/) (2018)
- [Evaluation of CNN-based Automatic Music Tagging Models](https://mlsanity.com/paper/2006.00751/) (2020)
- [Deep Learning and Music Adversaries](https://mlsanity.com/paper/1507.04761/) (2015)
- [Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition](https://mlsanity.com/paper/2104.06517/) (2021)
- [Reprogramming Language Models for Molecular Representation Learning](https://mlsanity.com/paper/2012.03460/) (2020)
- [Mood Classification Using Listening Data](https://mlsanity.com/paper/2010.11512/) (2020)

## Built on by (most cited first, 17 of 17)

- [Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition](https://mlsanity.com/paper/2310.06434/) (2023)
- [It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition](https://mlsanity.com/paper/2402.05457/) (2024)
- [SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts](https://mlsanity.com/paper/2306.02207/) (2023)
- [Sample-specific Masks for Visual Reprogramming-based Prompting](https://mlsanity.com/paper/2406.03150/) (2024)
- [Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition](https://mlsanity.com/paper/2309.15223/) (2023)
- [Bayesian-guided Label Mapping for Visual Reprogramming](https://mlsanity.com/paper/2410.24018/) (2024)
- [A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model](https://mlsanity.com/paper/2305.11244/) (2023)
- [Attribute-based Visual Reprogramming for Vision-Language Models](https://mlsanity.com/paper/2501.13982/) (2025)
- [Parameter-Efficient Learning for Text-to-Speech Accent Adaptation](https://mlsanity.com/paper/2305.11320/) (2023)
- [Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts](https://mlsanity.com/paper/2506.01000/) (2025)
- [Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction](https://mlsanity.com/paper/2506.04650/) (2025)
- [Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model](https://mlsanity.com/paper/2310.02971/) (2023)
- [MAIA: An Inpainting-Based Approach for Music Adversarial Attacks](https://mlsanity.com/paper/2509.04980/) (2025)
- [Differentially Private Adapters for Parameter Efficient Acoustic Modeling](https://mlsanity.com/paper/2305.11360/) (2023)
- [An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems](https://mlsanity.com/paper/2409.10515/) (2024)
- [BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming](https://mlsanity.com/paper/2606.24740/) (2026)
- [Reprogramming Vision-Language Models via Structured Prompt Reparameterization](https://mlsanity.com/paper/2609.36680/) (2026)

## Abstract (arXiv)

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset.