Indexing and Retrieving Voice Recordings by Instantly Tagging Mentioned Objects with Dots
The Internet of Things (IOT)(2019)
Delft Univ Technol | NEC Corp Ltd
Abstract
This paper presents a novel framework and its prototype tool for indexing and retrieving specific fragments of voice recordings obtained during discussions about physical objects such as text documents, pictures, or 3D models. When a specific part of an object is mentioned, it is tagged with an ink dot that is immediately registered in a database by capturing a microscopic image of the dot. Simultaneously, an index of the recording fragment is created and linked with the dot. After the recording, a dot can be scanned and identified by matching its microscopic image with the database to retrieve the linked recording fragment for playback. A handy tool was developed to facilitate these operations while the user concentrates on the ongoing discussion. Performance tests of the dot identification have shown genuine matches without error. In demonstrations of a realistic usage scenario, the tool successfully facilitated the creation of indexes with dots during a voice recording and correctly played back all the specific recording fragments linked to the dots.
MoreTranslated text
Key words
voice recording,indexing,retrieval,playback,pattern recognition,identification,image matching
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2003
被引用39 | 浏览
2018
被引用3 | 浏览
2018
被引用9 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest