259 research outputs found
Forecasting with time series imaging
Feature-based time series representations have attracted substantial
attention in a wide range of time series analysis methods. Recently, the use of
time series features for forecast model averaging has been an emerging research
focus in the forecasting community. Nonetheless, most of the existing
approaches depend on the manual choice of an appropriate set of features.
Exploiting machine learning methods to extract features from time series
automatically becomes crucial in state-of-the-art time series analysis. In this
paper, we introduce an automated approach to extract time series features based
on time series imaging. We first transform time series into recurrence plots,
from which local features can be extracted using computer vision algorithms.
The extracted features are used for forecast model averaging. Our experiments
show that forecasting based on automatically extracted features, with less
human intervention and a more comprehensive view of the raw time series data,
yields highly comparable performances with the best methods in the largest
forecasting competition dataset (M4) and outperforms the top methods in the
Tourism forecasting competition dataset
Comprehensive Study of Automatic Speech Emotion Recognition Systems
Speech emotion recognition (SER) is the technology that recognizes psychological characteristics and feelings from the speech signals through techniques and methodologies. SER is challenging because of more considerable variations in different languages arousal and valence levels. Various technical developments in artificial intelligence and signal processing methods have encouraged and made it possible to interpret emotions.SER plays a vital role in remote communication. This paper offers a recent survey of SER using machine learning (ML) and deep learning (DL)-based techniques. It focuses on the various feature representation and classification techniques used for SER. Further, it describes details about databases and evaluation metrics used for speech emotion recognition
Learning Local to Global Feature Aggregation for Speech Emotion Recognition
Transformer has emerged in speech emotion recognition (SER) at present.
However, its equal patch division not only damages frequency information but
also ignores local emotion correlations across frames, which are key cues to
represent emotion. To handle the issue, we propose a Local to Global Feature
Aggregation learning (LGFA) for SER, which can aggregate longterm emotion
correlations at different scales both inside frames and segments with entire
frequency information to enhance the emotion discrimination of utterance-level
speech features. For this purpose, we nest a Frame Transformer inside a Segment
Transformer. Firstly, Frame Transformer is designed to excavate local emotion
correlations between frames for frame embeddings. Then, the frame embeddings
and their corresponding segment features are aggregated as different-level
complements to be fed into Segment Transformer for learning utterance-level
global emotion features. Experimental results show that the performance of LGFA
is superior to the state-of-the-art methods.Comment: This paper has been accepted on INTERSPEECH 202
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
Generating and protecting against adversarial attacks for deep speech-based emotion recognition models
A paper in ICASSP 202
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