# Neuralogram: A Deep Neural Network Based Representation for Audio Signals

arXiv: https://arxiv.org/abs/1904.05073 · HTML: https://mlsanity.com/paper/1904.05073/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Audio Transformers](https://mlsanity.com/paper/2105.00335/) (2021)
- [A Generative Model for Raw Audio Using Transformer Architectures](https://mlsanity.com/paper/2106.16036/) (2021)
- [Attention is All You Need? Good Embeddings with Statistics are enough:Large Scale Audio Understanding without Transformers/ Convolutions/ BERTs/ Mixers/ Attention/ RNNs or ....](https://mlsanity.com/paper/2110.03183/) (2021)
- [Diverse Audio Embeddings -- Bringing Features Back Outperforms CLAP!](https://mlsanity.com/paper/2309.08751/) (2023)

## Abstract (arXiv)

We propose the Neuralogram -- a deep neural network based representation for understanding audio signals which, as the name suggests, transforms an audio signal to a dense, compact representation based upon embeddings learned via a neural architecture. Through a series of probing signals, we show how our representation can encapsulate pitch, timbre and rhythm-based information, and other attributes. This representation suggests a method for revealing meaningful relationships in arbitrarily long audio signals that are not readily represented by existing algorithms. This has the potential for numerous applications in audio understanding, music recommendation, meta-data extraction to name a few.