71,740 research outputs found
Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them
Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have certain percentage of overlaps, and so the overlap classification problem is an important part of audio classification. Nowadays, the work on overlapped audio event classification is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innovatively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic Latent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in classifying isolated audio events
Learning Audio Sequence Representations for Acoustic Event Classification
Acoustic Event Classification (AEC) has become a significant task for
machines to perceive the surrounding auditory scene. However, extracting
effective representations that capture the underlying characteristics of the
acoustic events is still challenging. Previous methods mainly focused on
designing the audio features in a 'hand-crafted' manner. Interestingly,
data-learnt features have been recently reported to show better performance. Up
to now, these were only considered on the frame-level. In this paper, we
propose an unsupervised learning framework to learn a vector representation of
an audio sequence for AEC. This framework consists of a Recurrent Neural
Network (RNN) encoder and a RNN decoder, which respectively transforms the
variable-length audio sequence into a fixed-length vector and reconstructs the
input sequence on the generated vector. After training the encoder-decoder, we
feed the audio sequences to the encoder and then take the learnt vectors as the
audio sequence representations. Compared with previous methods, the proposed
method can not only deal with the problem of arbitrary-lengths of audio
streams, but also learn the salient information of the sequence. Extensive
evaluation on a large-size acoustic event database is performed, and the
empirical results demonstrate that the learnt audio sequence representation
yields a significant performance improvement by a large margin compared with
other state-of-the-art hand-crafted sequence features for AEC
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
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