Tagspaces tag libraries
We explore the effects of training with different sized subsets of the training videos. We examine fully connected Deep Neural Networks (DNNs), AlexNet, VGG, Inception, and ResNet. We apply various CNN architectures to audio and investigate their ability to classify videos with a very large data set of 70M training videos (5.24 million hours) with 30,871 labels. Furthermore, methods to find descriptions for the mountains and hills are demonstrated.Ĭonvolutional Neural Networks (CNNs) have proven very effective in image classification and have shown promise for audio classification. Using a neural network algorithm, namely the self-organizing map, the music collection is organized and a novel visualization technique is facilitated to create the map of islands. An approach based on psychoacoustics is presented which focuses on the dynamic properties of music. The main challenge is to calculate the perceived similarity of two pieces of music. MP3s) without any further information such as which genres the pieces of music belong to. This thesis deals with the challenges involved in the automatic creation of such Islands of Music given only raw music data (e.g.
They could also serve as interfaces to digital music libraries, or they could simply be used to organize one's personal music collection at home. They could be utilized by music stores to assist their customers in nding something new to buy. Islands of Music are intended to support exploration of unknown music collections. To support navigation on the map the mountains and hills are labeled with words which describe rhythmic and other properties of the genres they represent. The pieces of music from the collection are placed on the map according to their genre. The islands are situated in such a way that similar genres are close together and might even be connected by a land passage while perceptually very different genres are separated by deep sea. Mountains and hills on these islands represent sub-genres. Islands of Music are a graphical user interface to music collections based on a metaphor of geographic maps. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms.
#Tagspaces tag libraries mac os#
The library is cross-platform and currently supports Linux, Mac OS X, and Windows systems. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. It contains an ex- tensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level mu- sic descriptors. We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. Finally, we provide useful insights on the effects of training dataset scale by testing hyper-parameter optimization on an industry-scale dataset. We then show potential positive downstream effects on the task of play prediction. We find that next-song recommendation quality of Word2Vec is anti-correlated with song popularity, and we show how song embedding optimization can balance performance across different popularity levels. We show that single-objective optimization can cause side effects on the non optimized metrics and propose a simple multi-objective optimization to mitigate these effects. We present new optimization objectives and metrics to monitor the effects of hyper-parameter optimization. In this work, we study the hyper-parameter optimization of behavioral song embeddings based on Word2Vec on a selection of downstream tasks, namely next-song recommendation, false neighbor rejection, and artist and genre clustering.
Song embeddings are a key component of most music recommendation engines.