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This paper proposes a long short-term memory recurrent neural network (LSTM-RNN) for extracting melody and simultaneously detecting regions of melody from polyphonic audio using the proposed harmonic sum loss. The previous state-of-the-art algorithms have not been based on machine learning techniques and certainly not on deep architectures. The harmonics structure in melody is incorporated in the...
By the number of people aged 60 or over and people with disabilities growing, homecare mobile robot draws increasing attention. However, there are challenges of autonomous navigation for homecare robot such as frequent changes of environment, obstacles and goal position. In this paper, we focus on verifying potential of neural network-based autonomous navigation for homecare mobile. And we compare...
This paper examines a novel binary feature referred to as the Local Hybrid Patterns (LHP) that is generated by mixing highly discriminative bits of the binary local pattern features (BLPFs) such as the Local Binary Patterns (LBP), Local Gradient Patterns (LGP), and Mean LBP (MLBP). Starting with the most discriminative BLPF selected, the LHP generating algorithm iteratively updates the bits of the...
This paper introduces a multi-class classification algorithm based on sparse representation which considers on rejection option to minimize risks caused by outliers. Here the outliers include signals that do not belong to any classes learned in a training step. To successfully reject the outliers, new rejection measure and corresponding dictionary learning algorithm are presented. Experimental results...
This paper studies a method for learning a discriminative visual codebook for various computer vision tasks such as image categorization and object recognition. The performance of various computer vision tasks depends on the construction of the codebook which is a table of visual-words (i.e. codewords). This paper proposed a learning criterion for constructing a discriminative codebook, and it is...
This paper considers a v-structured support vector machine (v-SSVM) which is a structured support vector machine (SSVM) incorporating an intuitive balance parameter v. In the absence of the parameter v, cumbersome validation would be required in choosing the balance parameter. We theoretically prove that the parameter v asymptotically converges to both the empirical risk of margin errors and the empirical...
This paper considers a large margin training of semi-Markov model (SMM) for phonetic recognition. The SMM framework is better suited for phonetic recognition than the hidden Markov model (HMM) framework in that the SMM framework is capable of simultaneously segmenting the uttered speech into phones and labeling the segment-based features. In this paper, the SMM framework is used to define a discriminant...
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