The performance of the Mel-Frequency Cepstrum Coefficients (MFCC) may be affected by (1) the number of filters, (2) the shape of filters, (3) the way in which filters are spaced, and (4) the way in which the power spectrum is warped. In this paper, several comparison experiments are done to find a best implementation. The traditional MFCC calculation excludes the 0th coefficient for the reason that it is regarded as somewhat unreliable. According to the analysis and experiments, the authors find that it can be regarded as the generalized frequency band energy (FBE) and is hence useful, which results in the FBE-MFCC. The authors also propose a better analysis, namely the auto-regressive analysis, on the frame energy,which outperforms its 1st and/or 2nd order differential derivatives. Experiments with the “863”Speech Database show that, compared with the traditional MFCC with its corresponding autoregressive analysis coefficients, the FBE-MFCC and the frame energy with their corresponding auto-regressive analysis coefficients form the best combination, reducing the Chinese syllable error rate (CSER) by about 10%, while the FBE-MFCC with the corresponding auto-regressive analysis coefficients reduces CSER by 2.5%. Comparison experiments are also done with a quite casual Chinese speech database, named Chinese Annotated Spontaneous Speech (CASS)corpus. The FBE-MFCC can reduce the error rate by about 2.9% on an average.

Comparison of Different Implementations of MFCC

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic data, and other modalities. While reviews have been written of some of this work before, no survey has been made of the approaches to evaluating approaches to MGR. This paper compiles a bibliography of work in MGR, and analyzes three aspects of evaluation: experimental designs, datasets, and figures of merit.

/pdf/a-survey-of-evaluation-in-music-genre-recognition-29v3l1icya.pdf

A survey of evaluation in music genre recognition

Electroencephalography (EEG) is another method for performing person identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while a person is performing a mental task such as motor control. However, few studies used EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEG-based PI using a deep learning (DL) approach. We proposed a cascade of DL using a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. We evaluated two types of RNNs, namely long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed method is evaluated on the state-of-the-art affective data set DEAP. The results indicate that CNN-GRU and CNN-LSTM can perform PI from different affective states and reach up to 99.90%–100% mean correct recognition rate. This significantly outperformed a support vector machine baseline system that used power spectral density features. Notably, the 100% mean CRR came from 32 subjects in DEAP data set. Even after the reduction of the number of EEG electrodes from 32 to 5 for more practical applications, the model could still maintain an optimal result obtained from the frontal region, reaching up to 99.17%. Amongst the two DL models, we found that CNN-GRU and CNN-LSTM performed similarly while CNN-GRU expended faster training time. In conclusion, the studied DL approaches overcame the influence of affective states in EEG-Based PI reported in the previous works.

Affective EEG-Based Person Identification Using the Deep Learning Approach

Introduction: Global Positioning Systems (GPS) offer many benefits in assisting school transportation operations achieve their mission. A variety of systems exist with varying capabilities. These systems offer not only positioning information in nearly real time and/or delayed time, but also vehicle position history, travel route, with speed and time, student identification, emergency alert, and automated accident notification. Recognizing that many systems are available that provide this information, and further, that this information is valuable in efficiently managing the school bus fleet, and further, that standardization of minimum information across the State of Michigan would be beneficial to school districts, the Pupil Transportation Advisory Committee recommends the Michigan Department of Education adopt the following as a recommended guideline for purchasing GPS equipment. ________________________________________________________________

Global Positioning Systems

While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. In this paper, we introduce a haptic texture database which allows for a systematic analysis of feature candidates. The publicly available database includes recorded accelerations measured during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 43 different textures. As a preliminary feature analysis, we test and compare six well-established features from audio and speech recognition together with a Gaussian Mixture Model-based classifier on our recorded free hand signals. Among the tested features, best results are achieved using Mel-Frequency Cepstral Coefficients (MFCCs), leading to a texture recognition accuracy of 80.2%.

A haptic texture database for tool-mediated texture recognition and classification

Music listening is a very personal and situational behavior for modern people who always carry smartphones in everyday life. Therefore, contextual information, such as user’s current activity and mood state could be used to greatly improve music recommendations. In this paper, we develop a smartphone-based mobile system that includes two core modules for recognizing human activities and then accordingly recommending music. In the proposed method, a deep residual bidirectional gated recurrent neural network is applied to obtain high activity recognition accuracy from accelerometer signals on the smartphone. In order to improve the performance of tempo-oriented music classification, an ensemble of dynamic classification using a long-term modulation spectrum and sequence classification using a short-term spectrogram is used. Music recommendation is performed using the relationship between the recognized human activities and the music files indexed by tempo-oriented music classification that reflects user preference models in order to achieve high user satisfaction. The results of comprehensive experiments on real data confirm the accuracy of the proposed activity-aware music recommendation framework.

Music Recommendation System Using Human Activity Recognition From Accelerometer Data

This paper proposes a high performance playout scheduling and packet loss concealment algorithm at the receiver for enhancing Voice over Internet Protocol (VoIP) speech quality. In the proposed method, arriving packets are classified by an adaptive thresholding approach based on the analysis of multiple features of short signal segments. The excellent classification results are used in the playout scheduling and packet loss concealment. In adaptive playout, the buffering time is minimized by way of playing out normally or compressing each packet according to accurate network jitter estimation. Additionally, linear prediction-based packet loss concealment delivers high voice quality by alleviating the metallic artifacts due to concealing consecutive packet loss or recovering lost packets1.

VoIP receiver-based adaptive playout scheduling and packet loss concealment technique

In this paper, we propose a high-performance audio fingerprinting system used in real-world query-by-example applications for acoustic audio-based content identification, especially for use in heterogeneous portable consumer devices or on-line audio distributed system. In the proposed method, audio fingerprints are generated using a modulated complex lapped transform-based non-repeating foreground audio extraction and an adaptive thresholding method for prominent peak detection. Effective matching is performed using a robust peak-pair-based hash function of non-repeating foreground audio to protect against noise, echo, artifacts from pitch-shifting, time-stretching, resampling, equalization, or compression. Experimental results confirm that the proposed method is quite robust in various distorted conditions and achieves preliminarily promising accuracy results.

Robust audio fingerprinting using peak-pair-based hash of non-repeating foreground audio in a real environment

https://onlinelibrary.wiley.com/doi/pdf/10.4218/etrij.17.0117.0157

Hyoung-Gook Kim

Papers

A haptic texture database for tool-mediated texture recognition and classification

Music Recommendation System Using Human Activity Recognition From Accelerometer Data

VoIP receiver-based adaptive playout scheduling and packet loss concealment technique

Robust audio fingerprinting using peak-pair-based hash of non-repeating foreground audio in a real environment

Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features