SBIR Phase II: Sound-object Recognition for Real-time or Offline Systems

这个小型企业创新研究（SBIR）第二阶段项目包括研究和开发用于离线和实时声音识别的音频识别和分析软件。 音乐家和音频工程师可以访问TB的音乐循环和声音效果。然而，音乐家仅限于使用纯文本关键字搜索来搜索声音。这是一个平凡的、不准确的和令人疲惫的过程，它忽略了文件的实际音频内容。所提出的解决方案提供了一个独特的“查找听起来像这个”的搜索引擎。媒体制作软件和硬件对于新手和高级用户来说都太复杂、乏味和劳动密集型。所提出的声音平台增加了以前缺少的功能-识别输入声音并自动为用户选择最佳参数。该项目使用信号处理和机器学习平台来执行新颖的实验，以实时分类音频流，提高识别准确性，并从大型集合中检索声音。将开发用于离线和实时声音识别的商业质量软件开发包。该项目集成了最先进的机器学习，数字信号处理和信息检索技术。如果成功，该平台将能够收听音频信号并理解它正在收听的内容-因为人类听众可以识别和分类声音。这项创新技术将授权给音频和音乐技术软件和硬件制造商。该平台适用于长期的发现和创新，已证明生物医学信号处理，安全/监控和互动游戏公司的商业利益。在第一个选择的市场中，（声音工程）平台将为音乐家，音乐爱好者和音频工程师带来直接的文化利益。它将使音乐创作和音频制作成为一项完全创造性的任务-最大限度地减少阻碍创作过程的繁琐技术问题，并降低新手音乐家和创意专业人士的进入门槛。

This Small Business Innovation Research (SBIR) Phase II project includes research and development of audio recognition and analysis software for offline and real-time sound recognition. Musicians and audio engineers have access to terabytes of music loops and sound effects. However, musicians are limited to searching for sounds using text-only keyword searches. This is a mundane, inaccurate, and exhausting process that ignores the files' actual audio content. The proposed solution provides a unique "find-something-that-sounds-like-this" search engine. Media production software and hardware is too complex, tedious, and labor-intensive for both novice and advanced users. The proposed sound platform adds capability that was previously missing - recognizing an input sound and automatically choosing the best parameters for the user. This project uses a signal processing and machine learning platform to perform novel experiments for classifying audio streams in real-time, improving recognition accuracy, and retrieving sounds from large collections. Commercial-quality software development kits for offline and real-time sound recognition will be developed. This project integrates state-of-the-art machine learning, digital signal processing, and information retrieval techniques. If successful, the platform will be able to listen to an audio signal and understand what it is listening to - as human listeners can identify and classify sounds. This innovative technology will be licensed to audio and music technology software and hardware manufacturers. The platform is suited for long-term discoveries and innovation, with demonstrated commercial interest from biomedical signal processing, security/surveillance, and interactive gaming companies. In the first chosen market, (sound engineering) the platform will have direct cultural benefits for musicians, music hobbyists, and audio engineers. It will allow music creation and audio production to become a completely creative task - minimizing the tedious technical issues that hinder the creative process, and lowering the barriers to entry for novice musicians and creative professionals.