International business machines corporation (20240127001). Audio Understanding with Fixed Language Models simplified abstract
Contents
- 1 Audio Understanding with Fixed Language Models
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Audio Understanding with Fixed Language Models - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Audio Understanding with Fixed Language Models
Organization Name
international business machines corporation
Inventor(s)
Kaizhi Qian of Champaign IL (US)
Yang Zhang of Cambridge MA (US)
Chuang Gan of Cambridge MA (US)
Zhenfang Chen of Cambridge MA (US)
Audio Understanding with Fixed Language Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127001 titled 'Audio Understanding with Fixed Language Models
Simplified Explanation
The patent application describes techniques for audio understanding using fixed language models.
- The system includes a fixed text embedder, pretrained audio encoder, and fixed autoregressive language model.
- The fixed text embedder converts prompt sequences into text embeddings.
- The pretrained audio encoder converts prompt sequences into audio embeddings.
- The fixed autoregressive language model answers new questions using text embeddings and audio embeddings.
Potential Applications
This technology could be applied in speech recognition systems, virtual assistants, and audio transcription services.
Problems Solved
This technology solves the problem of accurately understanding and responding to audio input in various applications.
Benefits
The benefits of this technology include improved accuracy in audio understanding tasks, enhanced user experience in voice-controlled devices, and increased efficiency in audio processing.
Potential Commercial Applications
Optimizing audio transcription services, enhancing virtual assistants, and improving speech recognition systems could be potential commercial applications of this technology.
Possible Prior Art
One possible prior art could be the use of fixed language models in natural language processing tasks, such as text generation and sentiment analysis.
Unanswered Questions
How does this technology compare to existing audio understanding systems in terms of accuracy and efficiency?
This article does not provide a direct comparison between this technology and existing audio understanding systems. Further research and testing would be needed to determine the performance differences.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address the potential limitations or challenges of implementing this technology. Factors such as computational resources, data privacy concerns, and model scalability could be important considerations in real-world deployment.
Original Abstract Submitted
techniques for audio understanding using fixed language models are provided. in one aspect, a system for performing audio understanding tasks includes: a fixed text embedder for, on receipt of a prompt sequence having (e.g., from 0-10) demonstrations of an audio understanding task followed by a new question, converting the prompt sequence into text embeddings; a pretrained audio encoder for converting the prompt sequence into audio embeddings; and a fixed autoregressive language model for answering the new question using the text embeddings and the audio embeddings. a method for performing audio understanding tasks is also provided.
