#Plexamp sonic analysis how to
#Plexamp sonic analysis upgrade

Secondly, doing sonic analysis ensures that the similarity reflects the actual music you have. While we are already using metadata for similar artists (and albums as well, although tbh we didn’t get a lot of data for this), needing to rely on metadata has two big downsides: There is a lot of music out there without metadata (think obscure Bandcamp artists or your high school band). The most obvious way the sonic data is useful is for showing similar artists, albums, and tracks. So what can we do with this new-found power? Quite a bit, it turns out… Sonic Similarity This allows us to see how “close” anything in your library is from anything else, where distance is based on a large number of sonic elements in the audio.

Once you enable Sonic analysis on a music library, all tracks, albums, and artists are processed in order to place them in a Musical Universe (math nerd? This musical universe consists of points in N-dimensional space). You can find some quick start info below or review all the juicy details in the support article.

#Plexamp sonic analysis how to

Skip down to the “Get Started” section to learn how to turn it on, or continue reading for all the gory and delightful details. We’ve added a sophisticated neural network which analyzes your music library, powering amazing new features and improving existing ones.

#Plexamp sonic analysis upgrade

Once this is complete, additional tests are performed to determine the validity and effectiveness of the approach.Today we’re announcing a massive server upgrade for Plex Pass subscribers who have discovered our awesome dedicated music app, Plexamp. Using the Python programming language and its machine learning library ‘Scikit Learn’, a rudimentary deep learning system is constructed to be trained on the sample spectrograms and learn to classify them. These samples are curated, edited and analysed to produce spectrograms that make up a dataset for neural network training. As described in this thesis, a sizeable bank of sound samples has been gathered – of individual musical notes from a selection of instruments – in pursuit of this application of the DeepDream architecture. The proposed musical version of this process involves analysing sound files and generating spectrograms – pictures of the sound that could be manipulated in much the same ways as regular images. This approach spawned an unexpectedly artistic process whereby feature recognition could be used to alter images in a dreamlike fashion, akin to seeing shapes in clouds. Originally, DeepDream came about as a way of visualising the weights inside neural network layers – matrices of variables containing the data that determines what information the network has learned – for better understanding of training and trouble-shooting of such networks that have been trained to classify images. In this thesis, construction of the analysis and feature recognition systems necessary for this long-term goal is achieved through the use of neural networks. This theoretical system would be designed to engage in stylistic feature transfer between existing musical pieces, and eventually to compose original music either autonomously or in collaboration with human musicians and composers. This thesis presents efforts to lay the foundations for an Artificial-Intelligence musical compositional system conceived on similar principles to DeepDream, a revolutionary computer vision process.