In this survey article, we give an overview of methods for music similarity estimation and recommendation based on context data. Unlike approaches that rely content have been researched almost two decades, music-context -based (or contextual ) to retrieval are a quite recent field research within information (MIR). Contextual data refers all music-relevant is not included in the audio signal itself. focus aspects primarily accessible through web technology. We discuss different sources...

An approach is presented to automatically build a search engine for large-scale music collections that can be queried through natural language. While existing approaches depend on explicit manual annotations and meta-data assigned the individual audio pieces, we derive descriptions by making use of methods from Web Retrieval Music Information Retrieval. Based ID3 tags collection mp3 files, retrieve relevant pages via Google queries contents these characterize pieces represent them term...

Digital humanism highlights the complex relationships between people, society, nature, and machines. It has been embraced by a growing community of individuals groups who are setting directions that may change current paradigms. Here we focus on initiatives generated Vienna Manifesto.

We present a novel, innovative user interface to music repositories. Given an arbitrary collection of digital files, our system creates virtual landscape which allows the freely navigate in this collection. This is accomplished by automatically extracting features from audio signal and training Self-Organizing Map (SOM) on them form clusters similar sounding pieces music. Subsequently, Smoothed Data Histogram (SDH) calculated SOM interpreted as three-dimensional height profile. profile...

We present a technique for combining audio signal-based music similarity with web-based musical artist to accelerate the task of automatic playlist generation. demonstrate applicability our proposed method by extending recently published interface players that benefits from intelligent structuring collections. While original approach involves calculation similarities between every pair songs in collection,we incorporate data reduce number necessary calculations. More precisely,we exploit...

The particularities of musical data and its multiple modalities make original contributions possible in many core RecSys topics such as content-based hybrid recommendation, user modeling, interfaces, context-aware mobile recommendations. But more urgently, the current revolution music industry represents major opportunities challenges for recommendation systems general. Recommendation are now central to streaming platforms, which rapidly increasing listenership becoming top source revenue...

The workshop features presentations of accepted contributions to the RecSys Challenge 2019 organized by trivago, TU Wien, Politecnico di Bari, and Karlsruhe Institute Technology. In challenge, which originates from domain online travel recommender systems, participants had build a click-prediction model based on user session interactions. Predictions were submitted in form list suggested accommodations evaluated an offline data set that contained information what accommodation was clicked...

A user interface to music repositories called nepTune creates a virtual landscape for an arbitrary collection of digital files, letting users freely navigate the collection. Automatically extracting features from audio signal and clustering pieces accomplish this. The helps generate 3D island landscape. rapidly growing research field information retrieval is developing technological foundations new generation more intelligent devices services. Researchers are creating algorithms analysis,...

Automatic drum transcription methods aim at extracting a symbolic representation of notes played by kit in audio recordings. For automatic music analysis, this task is particular interest as such transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In work, an approach transcribe drums from polyphonic signals based on recurrent neural network presented. Deep learning techniques like dropout data augmentation are...

This article comprehensively addresses the problem of similarity measurement between music artists via text-based features extracted from Web pages. To this end, we present a thorough evaluation different term-weighting strategies, normalization methods, aggregation functions, and techniques. In large-scale genre classification experiments carried out on real-world artist collections, analyze several thousand combinations settings/parameters that influence calculation process, investigate in...

Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed used to improve user experience increase revenue, e. g., through better-fitting recommendations more targeted marketing. Analyses of traces typically aim find traits age, gender, nationality derive common preferences. We investigate extent the music listening habits users platform Last.fm can be predict their nationality. propose a feature modeling approach...