---
title: Audio Moment Retrieval
url: https://www.emergentmind.com/topics/audio-moment-retrieval-amr
type: topic
---

# Audio Moment Retrieval

Audio Moment Retrieval (AMR) is the task of temporally localizing one or more relevant intervals ("moments") within long audio recordings given an external query—which may be free-form text, an audio snippet, or another modality such as video. The AMR formulation is characterized by its explicit prediction of the temporal boundaries $(t_{\mathrm{start}}, t_{\mathrm{end}})$ of segments in untrimmed audio streams that are relevant to the query, requiring models to perform fine-grained grounding rather than coarse-level clip selection. AMR subsumes and generalizes previous tasks such as query-by-example matching and segment-level retrieval, and exhibits direct analogies with video moment retrieval and temporal grounding in vision and multimodal research [2511.15131, 2409.15672, 2408.16990].

## 1. Formal Problem Definition

Let $X$ denote a continuous audio signal of duration $T$ seconds and $q$ a query (commonly free-form text, but in some settings audio or multimedia). The AMR task is to predict a set $y = \{y_n\}_{n=1}^N$, where each $y_n = (t^{n}_{\mathrm{start}}, t^{n}_{\mathrm{end}})$ is a subinterval $[t^{n}_{\mathrm{start}}, t^{n}_{\mathrm{end}}] \subset [0,T]$ best matched to $q$. The model computes relevance $s_i = f(A_i, q)$ for discretized candidate segments $A_i$ (e.g., generated by a sliding window of width $w$ and hop $h$), ranking and selecting the top-K predictions.

Alignment of predictions to annotated boundaries is evaluated using Intersection over Union (IoU):
\[
\mathrm{IoU}(\hat{y}, y) = \frac{|\ [\hat t_s,\hat t_e]\ \cap\ [t_s, t_e]\ |}{|\ [\hat t_s,\hat t_e]\ \cup\ [t_s, t_e]\ |}
\]
Metrics include Recall@$K$@$τ$ (fraction of queries for which at least one of the top-$K$ predicted moments achieves $\mathrm{IoU} \geq τ$ with any ground truth) and mean Average Precision (mAP) over IoU thresholds [2511.15131, 2409.15672, 2408.16990].

## 2. Dataset Construction and Annotation

Initial progress in AMR relied on synthetic datasets due to the lack of real-world benchmarks. "Clotho-Moment" [2409.15672] overlays captioned sound events on background city-soundstreams, providing large-scale simulated long recordings with moment labels. Manual evaluation was initially limited to 100 examples.

The emergence of CASTELLA [2511.15131]—a human-annotated corpus targeting AMR—has established a real-world benchmark. CASTELLA comprises 1,862 audio recordings (train: 1,009; validation: 213; test: 640) of 1–5 minutes each, annotated through a three-stage crowdsourcing process: (a) selection of up to five local moments per recording, (b) captioning (local and global, initially Japanese then translated and reviewed into English), and (c) temporal boundary marking at 1 s resolution, permitting overlaps. Statistics include 3,881 local captions, 11,308 timestamps, an average of 2.1 local captions per recording, and moment length distribution biased towards short events (<5 s).

In the context of audio-visual and cross-modal grounding, datasets such as HIREST [2412.13543] and MGSV-EC ("Ad-Moment") [2408.16990] advance the study of grounding queries (e.g., video snippets, text) to precise moments within long audio/music tracks—often using pseudo-labels generated by audio alignment for large-scale annotation.

## 3. Model Architectures and Methodologies

### DETR-Based Approaches

Most state-of-the-art AMR models adopt the detection-transformer (DETR) paradigm to handle set-based output and many-to-one query-to-moment associations. Notable instantiations include:

- **AM-DETR** [2409.15672]: Sliding-window audio and text features are fused via cross-attention encoders, followed by transformer decoders with $K$ learnable queries. Each decoder head outputs a confidence score and predicted center-width parameters, mapped to segment boundaries. Supervision is provided via Hungarian matching between predicted and ground-truth segments, using $\ell_1$ regression, generalized IoU, and classification (foreground/background) losses.

- **CASTELLA Baseline** [2511.15131]: Utilizes MS-CLAP for per-second audio embeddings and the CLAP text encoder, fuses modalities, and applies a DETR-style decoder. Pre-training on synthetic data (Clotho-Moment) is essential, with observed gains of 10.4 points in Recall@1@0.7 upon subsequent fine-tuning with human-annotated data. UVCOM architectures demonstrate superior performance, whereas Moment-DETR underperforms.

| Architecture      | Pre-trained | Fine-tuned | Recall@1@0.7 |
|-------------------|-------------|------------|--------------|
| QD-DETR           | synthetic   | ✗          | 5.8          |
| QD-DETR           | ✗           | CASTELLA   | 9.7          |
| QD-DETR           | synthetic   | CASTELLA   | 16.2         |
| UVCOM             | synthetic   | CASTELLA   | 20.3         |
| Moment-DETR       | synthetic   | CASTELLA   | 10.8         |

Performance degrades significantly for short events (<5 s), signifying continued limitations in temporal precision.

### Cross-modal and Self-Supervised Approaches

Systems such as QUAG [2412.13543] build on modality-synergistic perception (MSP)—leveraging symmetric InfoNCE loss to achieve global alignment between audio and other modalities (e.g., vision). Local fine-grained audio-visual interactions are modeled via cross-attention, followed by query-centric cognition (QC²) where deep query representations are used to filter both temporal and channel dimensions, dynamically masking irrelevant content. Ablations confirm that both global cross-modal alignment and query-selective filtering individually and jointly improve retrieval recall. QUAG achieves state-of-the-art R@0.5 = 72.54% and R@0.7 = 38.86% on HIREST.

In music and audio-visual grounding, methods employ two-stage pipelines (retrieval, then localization). For example, MaDe (ReaL) [2408.16990] first retrieves top candidate tracks using InfoNCE contrastive learning, and then predicts precise moment boundaries with a DETR-style decoder and alignment loss.

Query-by-example AMR has been addressed via robust audio fingerprinting systems [2210.08624], where CNNs with channel-wise spectral-temporal attention produce subfingerprints for windowed segments. Retrieval leverages locality-sensitive hashing for fast matching, followed by offset-consistency analysis for timestamp prediction, yielding localization accuracy within ±50 ms even under severe noise and reverb.

## 4. Training, Optimization, and Evaluation Protocols

Training procedures commonly use the AdamW optimizer with learning rates in $[1 \times 10^{-4}, 3 \times 10^{-4}]$, batch sizes around 32 (AMR), 512 (music retrieval), and train for up to 100 epochs with early stopping or scheduled decay [2511.15131, 2408.16990, 2409.15672]. Pre-training on synthetic data or with contrastive objectives is critical; models trained solely on real data underperform, as shown in empirical comparisons on CASTELLA. Fine-tuning with human-annotated data yields substantial performance gains.

Evaluation metrics for AMR include Recall@$K$@$τ$, with $τ$ typically set at 0.5 or 0.7, and mean Average Precision computed over a range of IoU thresholds. Comparisons on CASTELLA and HIREST demonstrate that real-data fine-tuning improves R@1@0.7 by over 10 points compared to synthetic-only training [2511.15131].

For query-by-example localization, estimators are evaluated by the proportion of predictions falling within ±50 ms of ground-truth boundaries, with accuracy remaining above 80% in noisy and reverberant conditions [2210.08624].

## 5. Variants, Modalities, and Generalizations

AMR extends naturally to text-audio, audio-audio, and multimodal queries, and is closely related to video moment retrieval, sound event detection (SED), and query-based video summarization. Approaches developed for video (e.g., detection transformers, attention-based grounding) are frequently adapted for AMR, with shared methodology including cross-modal contrastive pretraining, attention-based fusion, and temporal boundary regression [2511.15131, 2412.13543, 2409.15672].

Music moment retrieval, as in VMMR [2408.16990], formulates retrieval and localization for video queries aligned to background music, and shares architectural components with AMR. Extensions also consider environmental sound, speech, and audiobook retrieval, replacing encoders appropriately (e.g., wav2vec for speech).

## 6. Limitations, Challenges, and Future Directions

Despite rapid progress, several challenges remain. Datasets may exhibit language limitations (CASTELLA: English-only captions translated from Japanese), coarse temporal granularity (1 s resolution), and bias toward short moments [2511.15131]. Very brief event localization (<5 s) remains particularly challenging across architectures. Improving metric robustness, leveraging multilingual and multimodal annotations, and incorporating global scene context are noted directions for future benchmarks.

Research trajectories emphasize:

- Combining global captions and context with local retrieval [2511.15131].
- Integrating SED architectures and loss functions (e.g., contrastive triplet loss) for enhanced alignment.
- Improving transformer-based detectors with iterative refinement and learned temporal suppression [2409.15672].
- Scaling annotated datasets and supporting fine-grained, overlapping, and multi-query retrieval.
- Exploring domain transfer among AMR, video, music, and environmental sound retrieval [2408.16990, 2412.13543].

## 7. Summary Table of Notable AMR Datasets

| Dataset        | Size           | Data Type         | Annotation             | Key Usage                  |
|----------------|----------------|-------------------|------------------------|----------------------------|
| Clotho-Moment  | >40k clips     | Synthetic         | Simulated overlays     | Pre-training, ablation     |
| CASTELLA       | 1,862 recs     | Real, human-label | Multi-moment, 1 s res. | Real-world benchmark       |
| UnAV-100 sub   | 77 recs / 100q | Real, manual      | Single-moment, 1 s res.| Early manual eval          |
| MGSV-EC        | 4,050 tracks   | Music, video      | Pseudo-aligned         | Music/VMMR grounding       |
| HIREST         | —              | Video+Audio/Text  | Multi-modal, manual    | AV moment grounding        |

All evaluations and methodological advances underscore the centrality of transformer-based architectures with cross-modal attention, robust pre-training, and fine-grained annotations in advancing the state of Audio Moment Retrieval [2511.15131, 2409.15672, 2408.16990, 2412.13543, 2210.08624].

Source: https://www.emergentmind.com/topics/audio-moment-retrieval-amr