GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities

• URL: http://arxiv.org/abs/2406.11768v1
• Date: Mon, 17 Jun 2024 17:31:01 GMT
• Title: GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
• Authors: Sreyan Ghosh, Sonal Kumar, Ashish Seth, Chandra Kiran Reddy Evuru, Utkarsh Tyagi, S Sakshi, Oriol Nieto, Ramani Duraiswami, Dinesh Manocha,
• Abstract summary: General-purpose Large Audio-Language Model (LALM) with Advanced Audio Understanding and Complex Reasoning Abilities. We build GAMA by integrating an LLM with multiple types of audio representations, including features from a custom Audio Q-Former. We fine-tune GAMA on a large-scale audio-language dataset, which augments it with audio understanding capabilities.
• Score: 43.23351906406144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a novel General-purpose Large Audio-Language Model (LALM) with Advanced Audio Understanding and Complex Reasoning Abilities. We build GAMA by integrating an LLM with multiple types of audio representations, including features from a custom Audio Q-Former, a multi-layer aggregator that aggregates features from multiple layers of an audio encoder. We fine-tune GAMA on a large-scale audio-language dataset, which augments it with audio understanding capabilities. Next, we propose CompA-R (Instruction-Tuning for Complex Audio Reasoning), a synthetically generated instruction-tuning (IT) dataset with instructions that require the model to perform complex reasoning on the input audio. We instruction-tune GAMA with CompA-R to endow it with complex reasoning abilities, where we further add a soft prompt as input with high-level semantic evidence by leveraging event tags of the input audio. Finally, we also propose CompA-R-test, a human-labeled evaluation dataset for evaluating the capabilities of LALMs on open-ended audio question-answering that requires complex reasoning. Through automated and expert human evaluations, we show that GAMA outperforms all other LALMs in literature on diverse audio understanding tasks by margins of 1%-84%. Further, GAMA IT-ed on CompA-R proves to be superior in its complex reasoning and instruction following capabilities.

Related papers

• Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model [85.72664004969182]
  We introduce Step-Audio-AQAA, a fully end-to-end LALM designed for Audio Query-Audio Answer (AQAA) tasks.<n>The model integrates a dual-codebook audio tokenizer for linguistic and semantic feature extraction.<n>Our post-training approach employs interleaved token-output of text and audio to enhance semantic coherence.
  arXiv  Detail & Related papers  (2025-06-10T16:37:39Z)
• Multi-Domain Audio Question Answering Toward Acoustic Content Reasoning in The DCASE 2025 Challenge [102.84031769492708]
  This task defines three QA subsets to test audio-language models on interactive question-answering over diverse acoustic scenes.<n>Preliminary results on the development set are compared, showing strong variation across models and subsets.<n>This challenge aims to advance the audio understanding and reasoning capabilities of audio-language models toward human-level acuity.
  arXiv  Detail & Related papers  (2025-05-12T09:04:16Z)
• Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models [95.45204813682885]
  We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio tasks. We train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning. Our findings stress the core of structured CoT training in advancing audio reasoning.
  arXiv  Detail & Related papers  (2025-03-04T06:18:34Z)
• Audiopedia: Audio QA with Knowledge [0.0]
  We introduce Audiopedia, a novel task called Audio Question Answering with Knowledge. Unlike traditional Audio Question Answering (AQA) benchmarks that focus on simple queries answerable from audio alone, Audiopedia targets knowledge-intensive questions. We benchmark large audio language models (LALMs) on these sub-tasks and observe suboptimal performance. We propose a generic framework that can be adapted to any LALM, equipping them with knowledge reasoning capabilities.
  arXiv  Detail & Related papers  (2024-12-29T23:48:35Z)
• Audio Captioning via Generative Pair-to-Pair Retrieval with Refined Knowledge Base [0.0]
  Retrieval-Augmented Generation (RAG) retrieves audio-text pairs from a knowledge base and augments them with query audio to generate accurate textual responses. We propose generative pair-to-pair retrieval, which uses the generated caption as a text query to accurately find relevant audio-text pairs. Our approach achieves state-of-the-art results on benchmarks including AudioCaps, Clotho, and Auto-ACD.
  arXiv  Detail & Related papers  (2024-10-14T04:57:32Z)
• Beyond Single-Audio: Advancing Multi-Audio Processing in Audio Large Language Models [56.776580717999806]
  Real-world applications often involve processing multiple audio streams simultaneously. We propose the first multi-audio evaluation benchmark that consists of 20 datasets from 11 multi-audio tasks. We propose a novel multi-audio-LLM (MALLM) to capture audio context among multiple similar audios.
  arXiv  Detail & Related papers  (2024-09-27T12:06:53Z)
• Large Language Models Are Strong Audio-Visual Speech Recognition Learners [53.142635674428874]
  Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. We propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities. We evaluate our proposed approach on LRS3, the largest public AVSR benchmark, and we achieve new state-of-the-art results for the tasks of ASR and AVSR with a WER of 0.81% and 0.77%, respectively.
  arXiv  Detail & Related papers  (2024-09-18T21:17:27Z)
• Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models [0.9285295512807729]
  Audio Question Answering has garnered attention due to the advent of Large Audio Language Models. While LALMs excel in general audio understanding, they are limited in temporal reasoning. This paper addresses these challenges and limitations in audio temporal reasoning.
  arXiv  Detail & Related papers  (2024-09-10T05:26:53Z)
• DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding [51.32965203977845]
  We propose the use of discrete speech units (DSU) instead of continuous-valued speech encoder outputs. The proposed model shows robust performance on speech inputs from seen/unseen domains and instruction-following capability in spoken question answering. Our findings suggest that the ASR task and datasets are not crucial in instruction-tuning for spoken question answering tasks.
  arXiv  Detail & Related papers  (2024-06-13T17:28:13Z)
• AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs [27.122094554340194]
  We extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities. The resulting end-to-end model, named AudioChatLlama, can utilize audio prompts as a replacement for text and sustain a conversation.
  arXiv  Detail & Related papers  (2023-11-12T06:56:14Z)
• LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT [65.69648099999439]
  Generative Pre-trained Transformer (GPT) models have achieved remarkable performance on various natural language processing tasks. We propose LauraGPT, a novel unified audio-and-text GPT-based LLM for audio recognition, understanding, and generation.
  arXiv  Detail & Related papers  (2023-10-07T03:17:59Z)
• AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head [82.69233563811487]
  Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. We propose a multi-modal AI system named AudioGPT, which complements LLMs with foundation models to process complex audio information.
  arXiv  Detail & Related papers  (2023-04-25T17:05:38Z)

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.