PixelPlayer to Learn How Extract Musical Instrument

Massachusetts Institute of Technology scientists created new neural network
06 July 2018   1840

Scientist from MIT managed to create a neural network called PixelPlayer, which is able to indetify and extact the sound of individual musical instruments. The key feature of the development is the use of the method of spontaneous learning. This is reported by Analytics Vidhya.

In similar developments, the method of controlled learning was previously used. As input data, the AI ​​received marked audio files, the manual marking of which required a lot of time. PixelPlayer processes video - this allows to opt out of the preliminary preparation of information. Spontaneous training eliminated the human factor and accelerated the process.

Development involves three algorithms at once. The first processes the video, the second - the audio track, and the third synchronizes the data. PixelPlayer determines the sound pertaining to each pixel in the image. In this way, the neural network detects individual instruments and determines the melody to be released.

After 60 hours of training, the AI ​​was able to recognize with high accuracy individual melodies on new video recordings that had not been shown to it before. According to the developers, PixelPlayer is able to identify up to 20 different tools. This number can be increased by providing additional data for processing. Errors occur about trying to divide class-like instruments, for example, saxophone-alto and tenor.

PixelPlayer has already considerable potential for practical application. With this tool the quality of old live recordings can be improved. Amateur musicians often try to "remove" a certain party aurally, and the development of MIT scientists can simplify this task.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   468

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.