# Auditory Intelligence: Understanding the World Through Sound

arXiv: https://arxiv.org/abs/2508.07829 · HTML: https://mlsanity.com/paper/2508.07829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 52 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 30 of 39)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [Automated Audio Captioning with Recurrent Neural Networks](https://mlsanity.com/paper/1706.10006/) (2017)
- [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020)
- [A Framework for the Robust Evaluation of Sound Event Detection](https://mlsanity.com/paper/1910.08440/) (2019)
- [Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection](https://mlsanity.com/paper/2203.15296/) (2022)
- [FLAM: Frame-Wise Language-Audio Modeling](https://mlsanity.com/paper/2505.05335/) (2025)
- [STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events](https://mlsanity.com/paper/2206.01948/) (2022)
- [Integrating Frequency Translational Invariance in TDNNs and Frequency Positional Information in 2D ResNets to Enhance Speaker Verification](https://mlsanity.com/paper/2104.02370/) (2021)
- [FilterAugment: An Acoustic Environmental Data Augmentation Method](https://mlsanity.com/paper/2110.03282/) (2021)
- [Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event Detection](https://mlsanity.com/paper/2403.18638/) (2024)
- [Heavily Augmented Sound Event Detection utilizing Weak Predictions](https://mlsanity.com/paper/2107.03649/) (2021)
- [Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots](https://mlsanity.com/paper/2507.20530/) (2025)
- [Deep learning based cough detection camera using enhanced features](https://mlsanity.com/paper/2107.13260/) (2021)
- [Convolution-Based Channel-Frequency Attention for Text-Independent Speaker Verification](https://mlsanity.com/paper/2210.17310/) (2022)
- [VIFS: An End-to-End Variational Inference for Foley Sound Synthesis](https://mlsanity.com/paper/2306.05004/) (2023)

## Built on by (most cited first, 1 of 1)

- [CMDAR: A Chinese Multi-scene Dynamic Audio Reasoning Benchmark with Diverse Challenges](https://mlsanity.com/paper/2509.22461/) (2025)

## Abstract (arXiv)

Recent progress in auditory intelligence has yielded high-performing systems for sound event detection (SED), acoustic scene classification (ASC), automated audio captioning (AAC), and audio question answering (AQA). Yet these tasks remain largely constrained to surface-level recognition-capturing what happened but not why, what it implies, or how it unfolds in context. I propose a conceptual reframing of auditory intelligence as a layered, situated process that encompasses perception, reasoning, and interaction. To instantiate this view, I introduce four cognitively inspired task paradigms-ASPIRE, SODA, AUX, and AUGMENT-those structure auditory understanding across time-frequency pattern captioning, hierarchical event/scene description, causal explanation, and goal-driven interpretation, respectively. Together, these paradigms provide a roadmap toward more generalizable, explainable, and human-aligned auditory intelligence, and are intended to catalyze a broader discussion of what it means for machines to understand sound.