Learning a Metric for Music Similarity
نویسندگان
چکیده
This paper describe five different principled ways to embed songs into a Euclidean metric space. In particular, we learn embeddings so that the pairwise Euclidean distance between two songs reflects semantic dissimilarity. This allows distance-based analysis, such as for example straightforward nearest-neighbor classification, to detect and potentially suggest similar songs within a collection. Each of the six approaches (baseline, whitening, LDA, NCA, LMNN and RCA) rotate and scale the raw feature space with a linear transform. We tune the parameters of these models using a song-classification task with content-based features.
منابع مشابه
Composite Kernel Optimization in Semi-Supervised Metric
Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...
متن کاملLarge Scale Metric Learning for Music Similarity
Automatic music classification is an important problem in music analysis. In this paper, we use data from the “Million Song Dataset” to construct and evaluate music similarity metrics and metric learning techniques. While others have done metric learning on music datasets before, we evaluate which standard techniques perform best in both accuracy and time on this much larger dataset. We find th...
متن کاملAn Effective Approach for Robust Metric Learning in the Presence of Label Noise
Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...
متن کاملMirex 2011 Ams - Audio Similarity via Metric Learning
Our submissions (ML1, ML2, ML3) to the Audio Music Similarity (AMS) task are based upon learning an optimal distance metric over vector quantized MFCC histograms. ML1 is optimized to predict similarity derived from a collaborative filter; ML2 is optimized to predict genre similarity; ML3 is an unsupervised baseline which uses a native distance metric. This abstract details the system architectu...
متن کاملCombining Sources of Description for Approximating Music Similarity Ratings
In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the MagnaTagATune database. Using common formats for feature data, our approach can easily be transferred to other existing databas...
متن کاملExploratory Datamining in Music
This thesis deals with methods and techniques for music exploration, mainly focussing on the task of music retrieval. This task has become an important part of the modern music society in which music is distributed effectively via for example the Internet. This calls for automatic music retrieval and general machine learning in order to provide organization and navigation abilities. This Master...
متن کامل