Cross-Modal Audio Retrieval with Natural Language Queries

Table of Contents

1. Introduction

The rapid growth of multimedia data has created an urgent need for efficient retrieval systems across various modalities. While text, image, and video retrieval have seen significant advancements, audio retrieval using natural language queries remains largely unexplored. This research addresses this critical gap by introducing a novel framework for retrieving audio content using free-form natural language descriptions.

Traditional audio retrieval methods rely on metadata tags or audio-based queries, which limit expressiveness and usability. Our approach enables users to describe sounds using detailed natural language, such as "A man talking as music is playing followed by a frog croaking," allowing for more precise and intuitive retrieval of audio content that matches temporal event sequences.

2. Methodology

2.1 Benchmark Datasets

We introduce two challenging benchmarks based on AUDIO CAPS and Clotho datasets. AUDIO CAPS contains 10-second audio clips from AudioSet with human-written captions, while Clotho features 15-30 second audio clips from Freesound with detailed descriptions. These datasets provide rich audio-text pairs essential for training cross-modal retrieval systems.

2.2 Cross-Modal Retrieval Framework

Our framework adapts video retrieval architectures for audio retrieval, leveraging pre-trained audio expert networks. The system learns joint embeddings where similar audio and text representations are mapped close together in a shared latent space.

2.3 Pre-training Strategy

We demonstrate the benefits of pre-training on diverse audio tasks, showing that transfer learning from related domains significantly improves retrieval performance. The ensemble of audio experts captures complementary aspects of audio content.

3. Technical Implementation

3.1 Audio Feature Extraction

We employ multiple pre-trained audio networks to extract rich feature representations. The audio embedding $\mathbf{a}_i$ for clip $i$ is computed as:

$$\mathbf{a}_i = f_{\theta}(x_i)$$

where $f_{\theta}$ represents the audio encoder and $x_i$ is the raw audio input.

3.2 Text Encoding

Text queries are encoded using transformer-based models to capture semantic meaning. The text embedding $\mathbf{t}_j$ for query $j$ is:

$$\mathbf{t}_j = g_{\phi}(q_j)$$

where $g_{\phi}$ is the text encoder and $q_j$ is the input query.

3.3 Cross-Modal Alignment

We optimize the similarity between audio and text embeddings using contrastive learning. The similarity score $s_{ij}$ between audio $i$ and text $j$ is computed as:

$$s_{ij} = \frac{\mathbf{a}_i \cdot \mathbf{t}_j}{\|\mathbf{a}_i\| \|\mathbf{t}_j\|}$$

The model is trained to maximize similarity for matching pairs and minimize it for non-matching pairs.

4. Experimental Results

4.1 Baseline Performance

Our experiments establish strong baselines for text-based audio retrieval. The models achieve promising results on both AUDIO CAPS and Clotho benchmarks, with retrieval accuracy measured using standard metrics including Recall@K and Mean Average Precision.

4.2 Ensemble Methods

We show that combining features from multiple pre-trained audio networks through ensemble learning improves retrieval robustness. Different networks capture complementary aspects of audio content, leading to more comprehensive representations.

4.3 Ablation Studies

Ablation experiments validate the importance of each component in our framework. The studies reveal that both the choice of audio encoder and the cross-modal alignment strategy significantly impact final performance.

5. Analysis Framework

Core Insight

This research fundamentally challenges the audio retrieval status quo by shifting from metadata-dependent systems to content-based natural language querying. The approach represents a paradigm shift comparable to what CycleGAN (Zhu et al., 2017) achieved for unpaired image translation—breaking the dependency on strictly paired training data through cross-modal alignment.

Logical Flow

The methodology follows a sophisticated three-stage pipeline: feature extraction from diverse audio experts, semantic encoding of free-form text, and cross-modal embedding alignment. This architecture mirrors the success of CLIP (Radford et al., 2021) in vision-language domains but adapts it specifically for audio's temporal and spectral characteristics.

Strengths & Flaws

Strengths: The ensemble approach cleverly leverages existing audio expertise rather than training from scratch. The benchmark creation addresses a critical data scarcity issue in the field. The computational efficiency for video retrieval applications is particularly compelling.

Flaws: The approach inherits limitations from its component networks—potential biases in pre-training data, limited generalization to rare sound events, and sensitivity to textual paraphrasing. The temporal alignment between text descriptions and audio events remains challenging for longer sequences.

Actionable Insights

For practitioners: Start with fine-tuning the ensemble approach on domain-specific audio data. For researchers: Focus on improving temporal modeling and addressing the paraphrase robustness issue. The framework shows immediate applicability for audio archive search and video retrieval acceleration.

Case Study: Audio Archive Search

Consider a historical audio archive containing thousands of unlabeled environmental recordings. Traditional keyword-based search fails because the content isn't tagged. Using our framework, archivists can query "heavy rainfall with distant thunder" and retrieve relevant clips based on audio content rather than metadata.

6. Future Applications

The technology enables numerous practical applications including:

• Intelligent Audio Archives: Enhanced search capabilities for historical sound collections like the BBC Sound Effects Archive
• Low-power IoT Devices: Audio-based monitoring systems for conservation and biological research
• Creative Applications: Automated sound effect matching for podcasts, audiobooks, and multimedia production
• Accessibility Tools: Audio description and retrieval systems for visually impaired users
• Video Retrieval Acceleration: Using audio as a proxy for video content in large-scale search systems

Future research directions include extending to multilingual queries, improving temporal reasoning capabilities, and developing more efficient cross-modal alignment techniques suitable for real-time applications.

7. References

1. Zhu, J. Y., et al. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. IEEE ICCV.
2. Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. ICML.
3. Gemmeke, J. F., et al. (2017). Audio Set: An ontology and human-labeled dataset for audio events. IEEE ICASSP.
4. Drossos, K., et al. (2020). Clotho: An Audio Captioning Dataset. IEEE ICASSP.
5. Oncescu, A. M., et al. (2021). Audio Retrieval with Natural Language Queries. INTERSPEECH.
6. Arandjelovic, R., & Zisserman, A. (2018). Objects that sound. ECCV.
7. Harvard Dataverse: Audio Retrieval Benchmarks