ALM2Vec: Learning Audio Embeddings for Universal Audio Retrieval with Large Audio-Language Models

• URL: http://arxiv.org/abs/2606.30682v1
• Date: Sat, 27 Jun 2026 03:56:57 GMT
• Title: ALM2Vec: Learning Audio Embeddings for Universal Audio Retrieval with Large Audio-Language Models
• Abstract summary: We present ALM2Vec, a universal audio embedding framework derived from pretrained large audio--language models (LALMs)<n>By transferring the audio understanding, instruction-following, and reasoning capabilities, ALM2Vec learns a unified embedding space for retrieval across audio domains and task types.
• Score: 10.50405619906082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in language--audio retrieval have been largely driven by contrastive dual-encoder architectures that align audio and text in a shared embedding space. While effective, existing retrieval embeddings are primarily optimized for audio--caption matching, limiting their ability to support diverse retrieval objectives and controllable retrieval behaviors. We present ALM2Vec, a universal audio embedding framework derived from pretrained large audio--language models (LALMs). By transferring the audio understanding, instruction-following, and reasoning capabilities acquired through large-scale multimodal training, ALM2Vec learns a unified embedding space for retrieval across audio domains and task types. Beyond conventional text--audio retrieval, ALM2Vec incorporates natural-language instructions into the embedding process, enabling instruction-aware retrieval for scenarios such as audio question answering and aspect-conditioned retrieval. Experimental results show that ALM2Vec achieves competitive performance on standard audio and speech retrieval benchmarks while exhibiting promising compositional and controllable retrieval capabilities, highlighting its potential as a unified audio embedding model for retrieval across domains, tasks, and user intents.

Related papers

• ATIR: Towards Audio-Text Interleaved Contextual Retrieval [63.68521448682396]
  We introduce the Audio-Text Interleaved contextual Retrieval (ATIR) task, where queries can alternate between audio and text modalities.<n>This benchmark substantially addresses the limitations of existing audio retrieval datasets in semantic retrieval.
  arXiv  Detail & Related papers  (2026-04-22T07:11:58Z)
• Step-Audio 2 Technical Report [120.58375054866815]
  Step-Audio 2 is an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation.<n>By integrating a latent audio encoder and reasoning-centric reinforcement learning (RL), Step-Audio 2 achieves promising performance in automatic speech recognition (ASR) and audio understanding.
  arXiv  Detail & Related papers  (2025-07-22T14:23:55Z)
• USAD: Universal Speech and Audio Representation via Distillation [56.91647396619358]
  Universal Speech and Audio Distillation (USAD) is a unified approach to audio representation learning.<n>USAD integrates diverse audio types - speech, sound, and music - into a single model.
  arXiv  Detail & Related papers  (2025-06-23T17:02:00Z)
• From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data [55.2480439325792]
  Audio-aware large language models (ALLMs) have recently made great strides in understanding and processing audio inputs.<n>These models are typically adapted from text-based large language models (LLMs) through additional training on audio-related tasks.<n>We propose a data generation framework that produces contrastive-like training data, designed to enhance ALLMs' ability to differentiate between present and absent sounds.
  arXiv  Detail & Related papers  (2025-05-26T16:08:41Z)
• IFEval-Audio: Benchmarking Instruction-Following Capability in Audio-based Large Language Models [18.11667976818302]
  IFEval-Audio contains 280 audio-instruction-answer triples across six diverse dimensions.<n>Each example pairs an audio input with a text instruction, requiring the model to generate an output that follows a specified structure.<n>We benchmark state-of-the-art audio LLMs on their ability to follow audio-involved instructions.
  arXiv  Detail & Related papers  (2025-05-22T15:15:29Z)
• Auto-ACD: A Large-scale Dataset for Audio-Language Representation Learning [50.28566759231076]
  We propose an innovative, automatic approach to establish an audio dataset with high-quality captions. Specifically, we construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.5M audio-text pairs. We employ LLM to paraphrase a congruent caption for each audio, guided by the extracted multi-modality clues.
  arXiv  Detail & Related papers  (2023-09-20T17:59:32Z)
• Separate Anything You Describe [53.30484933564858]
  Language-queried audio source separation (LASS) is a new paradigm for computational auditory scene analysis (CASA)<n>AudioSep is a foundation model for open-domain audio source separation with natural language queries.
  arXiv  Detail & Related papers  (2023-08-09T16:09:44Z)
• Contrastive Audio-Language Learning for Music [13.699088044513562]
  MusCALL is a framework for Music Contrastive Audio-Language Learning. Our approach consists of a dual-encoder architecture that learns the alignment between pairs of music audio and descriptive sentences.
  arXiv  Detail & Related papers  (2022-08-25T16:55:15Z)
• Joint Speech Recognition and Audio Captioning [37.205642807313545]
  Speech samples recorded in both indoor and outdoor environments are often contaminated with secondary audio sources. We aim to bring together the growing field of automated audio captioning (AAC) and the thoroughly studied automatic speech recognition (ASR) We propose several approaches for end-to-end joint modeling of ASR and AAC tasks.
  arXiv  Detail & Related papers  (2022-02-03T04:42:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.