Recommendation systems are essential for music platforms to drive exploration and discovery for users. Little work has been done in exploring cross-language music recommendation systems, which represent another avenue for music exploration. In this paper, we collected and created a database of over 200,000 artists, which includes subsets of artists that sing in 8 different languages other than English. Our goal was to recommend artists in those 8 other language subsets for a given English-speaking artist. Using Spotify’s API-related artists feature, we implemented two approaches: a matrix factorization model using alternating least squares and a breadth-first search system. Both systems perform significantly better than a random baseline based on accuracy of the base artist’s genre with the breadth-first search model outperforming the matrix factorization technique. We conclude with suggestions for improving the performance and reach of cross-language music recommendation systems.

Cities with strong local music scenes enjoy many social and economic benefits. To this end, we are interested in developing a locally-focused artist and event recommendation system called Localify.org that supports and promotes local music scenes. In this demo paper, we describe both the overall system architecture as well as our core recommendation algorithm. This algorithm uses artist-artist similarity information, as opposed to user-artist preference information, to bootstrap recommendation while we grow the number of users. The overall design of Localify was chosen based on the fact that local artists tend to be relatively obscure and reside in the long tail of the artist popularity distribution. We discuss the role of popularity bias and how we attempt to ameliorate it in the context of local music recommendation.

Singing songs can be an engaging and effective activity when learning a foreign language. In this paper, we describe a multi-language karaoke application called SLIONS: Singing and Listening to Improve Our Natural Speaking. When developing this application, we followed a user-centered design process which was informed by conducting interviews with domain experts, extensive usability testing, and reviewing existing gamified karaoke and language learning applications. The key feature of SLIONS is that we used automatic speech recognition (ASR) to provide students with personalized, granular feedback based on their singing pronunciation. We also provided multi-modal instruction: audio of music and singing tracks, video of a professional singer and translated text of lyrics to help students learn and master each song in the foreign language. To test the efficacy of SLIONS, we conducted a one-week pilot study with English and Chinese language learning students (N=15). The initial quantitative results show that our application can improve pronunciation and may improve vocabulary. In addition, the qualitative feedback from the students suggests that SLIONS is both fun to use and motivates students to practice speaking and singing in a foreign language.

We present the Beat-Sync-Mash-Coder, a new tool for semi-automated real-time creation of beat-synchronous music mashups. We combine phase vocoder and beat tracker technology to automate the task of synchronizing clips. Freeing the user from this task allows us to replace the traditional audio editing paradigm of the Digital Audio Workstation with an intuitive clip selection interface. The application is completely web-based and operates in the ubiquitous cross-platform Flash framework. The efficiency of our implementation is reflected in performance tests, which demonstrate that the system can sustain real-time phase vocoding of 5-9 simultaneous audio signals on consumer-level hardware. This allows the user to easily create dynamic, intricate and musically coherent acoustic soundscapes. Based on an initial user study with 24 high school students, we also find that the Beat-Sync-Mash-Coder is engaging and can get students excited about music and technology.

Associating labels with online products can be a labor-intensive task. We study the extent to which a standard "bag of visual words" image classifier can be used to tag products with useful information, such as whether a sneaker has laces or velcro straps. Using Scale Invariant Feature Transform (SIFT) image descriptors at random keypoints, a hierarchical visual vocabulary, and a variant of nearest-neighbor classification, we achieve accuracies between 66% and 98% on 2- and 3-class classification tasks using several dozen training examples. We also increase accuracy by combining information from multiple views of the same product.

When attempting to annotate music, it is important to consider both acoustic content and social context. This paper explores techniques for collecting and combining multiple sources of such information for the purpose of building a query-by-text music retrieval system. We consider two representations of the acoustic content (related to timbre and harmony) and two social sources (social tags and web documents). We then compare three algorithms that combine these information sources: calibrated score averaging (CSA), RankBoost, and kernel combination support vector machines (KC-SVM). We demonstrate empirically that each of these algorithms is superior to algorithms that use individual information sources.

We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-based query. We consider the related tasks of content-based audio annotation and retrieval as one supervised multiclass, multilabel problem in which we model the joint probability of acoustic features and words. We collect a data set of 1700 human-generated annotations that describe 500 Western popular music tracks. For each word in a vocabulary, we use this data to train a Gaussian mixture model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies expectation maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality of the music annotations produced by our system is comparable with the performance of humans on the same task. Our ldquoquery-by-textrdquo system can retrieve appropriate songs for a large number of musically relevant words. We also show that our audition system is general by learning a model that can annotate and retrieve sound effects.

We improve upon query-by-example for content-based audio information retrieval by ranking items in a database based on semantic similarity, rather than acoustic similarity, to a query example. The retrieval system is based on semantic concept models that are learned from a training data set containing both audio examples and their text captions. Using the concept models, the audio tracks are mapped into a semantic feature space, where each dimension indicates the strength of the semantic concept. Audio retrieval is then based on ranking the database tracks by their similarity to the query in the semantic space. We experiment with both semantic- and acoustic-based retrieval systems on a sound effects database and show that the semantic-based system improves retrieval both quantitatively and qualitatively.

Query-by-semantic-description (QBSD) is a natural paradigm for retrieving content from large databases of music. A major impediment to the development of good QBSD systems for music information retrieval has been the lack of a cleanly-labeled, publicly-available, heterogeneous data set of songs and associated annotations. We have collected the Computer Audition Lab 500-song (CAL500) data set by having humans listen to and annotate songs using a survey designed to capture ‘semantic associations ’ between music and words. We adapt the supervised multi-class labeling (SML) model, which has shown good performance on the task of image retrieval, and use the CAL500 data to learn a model for music retrieval. The model parameters are estimated using the weighted mixture hierarchies expectationmaximization algorithm which has been specifically designed to handle real-valued semantic association between words and songs, rather than binary class labels. The output of the SML model, a vector of class-conditional probabilities, can be interpreted as a semantic multinomial distribution over a vocabulary. By also representing a semantic query as a query multinomial distribution, we can quickly rank order the songs in a database based on the Kullback-Leibler divergence between the query multinomial and each song’s semantic multinomial. Qualitative and quantitative results demonstrate that our SML model can both annotate a novel song with meaningful words and retrieve relevant songs given a multi-word, text-based query.

This paper explores the automatic classification of audio tracks into musical genres. Our goal is to achieve human-level accuracy with fast training and classification. This goal is achieved with radial basis function (RBF) networks by using a combination of unsupervised and supervised initialization methods. These initialization methods yield classifiers that are as accurate as RBF networks trained with gradient descent (which is hundreds of times slower). In addition, feature subset selection further reduces training and classification time while preserving classification accuracy. Combined, our methods succeed in creating an RBF network that matches the musical classification accuracy of humans. The general algorithmic contribution of this paper is to show experimentally that RBF networks initialized with a combination of methods can yield good classification performance without relying on gradient descent. The simplicity and computational efficiency of our initialization methods produce classifiers that are fast to train as well as fast to apply to novel data. We also present an improved method for initializing the k-means clustering algorithm, which is useful for both unsupervised and supervised initialization methods.