39,629 research outputs found
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
Gaussian process (GP) audio source separation is a time-domain approach that
circumvents the inherent phase approximation issue of spectrogram based
methods. Furthermore, through its kernel, GPs elegantly incorporate prior
knowledge about the sources into the separation model. Despite these compelling
advantages, the computational complexity of GP inference scales cubically with
the number of audio samples. As a result, source separation GP models have been
restricted to the analysis of short audio frames. We introduce an efficient
application of GPs to time-domain audio source separation, without compromising
performance. For this purpose, we used GP regression, together with spectral
mixture kernels, and variational sparse GPs. We compared our method with
LD-PSDTF (positive semi-definite tensor factorization), KL-NMF
(Kullback-Leibler non-negative matrix factorization), and IS-NMF (Itakura-Saito
NMF). Results show that the proposed method outperforms these techniques.Comment: Paper submitted to the 44th International Conference on Acoustics,
Speech, and Signal Processing, ICASSP 2019. To be held in Brighton, United
Kingdom, between May 12 and May 17, 201
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Albeit, the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be straightforwardly transformed to the implicit case if
scalability should be maintained. There are few if any implicit feedback
benchmark datasets, therefore new ideas are usually experimented on explicit
benchmarks. In this paper, we propose a generic context-aware implicit feedback
recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor
factorization learning method that scales linearly with the number of non-zero
elements in the tensor. The method also allows us to incorporate diverse
context information into the model while maintaining its computational
efficiency. In particular, we present two such context-aware implementation
variants of iTALS. The first incorporates seasonality and enables to
distinguish user behavior in different time intervals. The other views the user
history as sequential information and has the ability to recognize usage
pattern typical to certain group of items, e.g. to automatically tell apart
product types or categories that are typically purchased repetitively
(collectibles, grocery goods) or once (household appliances). Experiments
performed on three implicit datasets (two proprietary ones and an implicit
variant of the Netflix dataset) show that by integrating context-aware
information with our factorization framework into the state-of-the-art implicit
recommender algorithm the recommendation quality improves significantly.Comment: Accepted for ECML/PKDD 2012, presented on 25th September 2012,
Bristol, U
- …