Feature Learning from Spectrograms for Assessment of Personality Traits

Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted speech processing features extracted with off-the-shelf toolboxes. To achieve high accuracy, numerous features are typically extracted using complex and highly parameterized algorithms. In this paper, a new method based on feature learning and spectrogram analysis is proposed to simplify the feature extraction process while maintaining a high level of accuracy. The proposed method learns a dictionary of discriminant features from patches extracted in the spectrogram representations of training speech segments. Each speech segment is then encoded using the dictionary, and the resulting feature set is used to perform classification of personality traits. Experiments indicate that the proposed method achieves state-of-the-art results with a significant reduction in complexity when compared to the most recent reference methods. The number of features, and difficulties linked to the feature extraction process are greatly reduced as only one type of descriptors is used, for which the 6 parameters can be tuned automatically. In contrast, the simplest reference method uses 4 types of descriptors to which 6 functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure

Melodic Transcription of Flamenco Singing from Monophonic and Polyphonic Music Recordings

We propose a method for the automatic transcription of flamenco singing from monophonic and polyphonic music recordings. Our transcription system is based on estimating the fundamental frequency (f0) of the singing voice, and follows an iterative strategy for note segmentation and labelling. The generated transcriptions are used in the context of melodic similarity, style classification and pattern detection. In our study, we discuss the difficulties found in transcribing flamenco singing and in evaluating the obtained transcriptions, we analyze the influence of the different steps of the algorithm, and we state the main limitations of our approach and discuss the challenges for future studies