Audio-track classification using deep neural networks

使用深度神经网络进行音轨分类

• 批准号: 530283-2018
• 负责人: Shirani, Shahram
• 金额: $ 1.82万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665075
• 关键词: Audio; track; classification; using; deep

Bell Media Inc. is the mass media subsidiary of Bell Canada Enterprises. A significant problem faced by the company is the absence of an efficient audio-track identification scheme. The audio-tracks of Bell digital media could be stereo, surround sound packages, mono or speech (described video). Although there are standards for tagging audio channels with metadata, many media production facilities (including Bell) do not adhere to these standards. In fact, in many cases there is no metadata tags, or the tracks are not labeled properly. Currently the company uses a manual approach to classify unlabeled audio tracks, which is inefficient, and time consuming. The objective of this project therefore, is to develop a new deep neural network based audio track identification that Bell can use on its audio tracks effectively and efficiently. Labeling the audio tracks will allow Bell to increase the amount of automation in their workflow, thereby increasing efficiency and accuracy, while at the same time improving their overall product development.****

贝尔媒体公司是加拿大贝尔公司的大众传媒子公司该公司面临的一个重大问题是缺乏有效的音轨识别方案。贝尔数字媒体的音轨可以是立体声，环绕声包，单声道或语音（描述视频）。虽然有用元数据标记音频频道的标准，但许多媒体制作设施（包括贝尔）并不遵守这些标准。事实上，在许多情况下，没有元数据标签，或者曲目没有正确标记。目前，该公司使用手动方法对未标记的音轨进行分类，这是低效且耗时的。因此，该项目的目标是开发一种新的基于深度神经网络的音轨识别，贝尔可以有效地使用其音轨。对音轨进行标记将使Bell能够提高工作流程的自动化程度，从而提高效率和准确性，同时改善其整体产品开发。