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

Popular music is often composed of an accompaniment and a lead component, the latter typically consisting of vocals. Filtering such mixtures to extract one or both components has many applications, such as automatic karaoke and remixing. This particular case of source separation yields very specific challenges and opportunities, including the particular complexity of musical structures, but also relevant prior knowledge coming from acoustics, musicology or sound engineering. Due to both its importance in applications and its challenging difficulty, lead and accompaniment separation has been a popular topic in signal processing for decades. In this article, we provide a comprehensive review of this research topic, organizing the different approaches according to whether they are model-based or data-centered. For model-based methods, we organize them according to whether they concentrate on the lead signal, the accompaniment, or both. For data-centered approaches, we discuss the particular difficulty of obtaining data for learning lead separation systems, and then review recent approaches, notably those based on deep learning. Finally, we discuss the delicate problem of evaluating the quality of music separation through adequate metrics and present the results of the largest evaluation, to-date, of lead and accompaniment separation systems. In conjunction with the above, a comprehensive list of references is provided, along with relevant pointers to available implementations and repositories.

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01766781/document

An Overview of Lead and Accompaniment Separation in Music

Global positioning systems

An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).

https://rua.ua.es/dspace/bitstream/10045/72592/1/2018_Pires_etal_Sensors.pdf

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review.

Recent research on speech segregation and music fingerprinting has led to improvements in speech segregation and music identification algorithms. Speech and music segregation generally involves the identification of music followed by speech segregation. However, music segregation becomes a challenging task in the presence of noise. This paper proposes a novel method of speech segregation for unlabelled stationary noisy audio signals using the deep belief network (DBN) model. The proposed method successfully segregates a music signal from noisy audio streams. A recurrent neural network (RNN)-based hidden layer segregation model is applied to remove stationary noise. Dictionary-based fisher algorithms are employed for speech classification. The proposed method is tested on three datasets (TIMIT, MIR-1K, and MusicBrainz), and the results indicate the robustness of proposed method for speech segregation. The qualitative and quantitative analysis carried out on three datasets demonstrate the efficiency of the proposed method compared to the state-of-the-art speech segregation and classification-based methods.

/pdf/a-hybrid-technique-for-speech-segregation-and-classification-13qsz4vo7s.pdf

A hybrid technique for speech segregation and classification using a sophisticated deep neural network.

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

Nowadays, user localization in indoor environments is more necessary to build many location-based services. This paper presents a robust audio identification method for enhancing a real-time indoor localization system on a mobile device using the audio signals emitted by nearby loudspeakers. The proposed audio identification method deals with various noise distortions due to different noisy indoor locations by using foreground/background audio separation, prominent spectral pitch-based binary audio fingerprinting, and spectral peak-triplet-based audio fingerprinting. Experimental results confirm that the proposed audio identification method is quite robust in different noise conditions and achieves preliminary promising results for discriminating the location and orientation of a user in large indoor locations.

A robust audio identification for enhancing audio-based indoor localization

This paper proposes a robust TV advertisement search based on audio fingerprinting in real environments. To obtain prominent audio peak pairs against different types of distortions, an adaptive thresholding method based on a median filter and peak-picking update is applied. Using the prominent audio peak-pair hashing, the proposed audio fingerprinting algorithm improves the robustness of the TV advertisement search against noise, pitch-shifting, and time-stretching, and delivers high identification accuracy in spite of short queries. The experimental results show that the proposed method has respectable results compared to other methods

TV Advertisement Search Based on Audio Peak-Pair Hashing in Real Environments

Separating the leading singing voice from the musical background from a monaural recording is a challenging task that appears naturally in several music processing applications. Recently, kernel additive modeling with generalized spatial Wiener filtering (GW) was presented for music/voice separation. In this paper, an adaptive auditory filtering based on β-order minimum mean-square error spectral amplitude estimation (bSA) is applied to the kernel additive modeling for improving the singing voice separation performance from monaural music signal. The proposed algorithm is composed of five modules: short time Fourier transform, music/voice separation based on bSA, determination of back-fitting, back-fitting, and inverse short time Fourier transform. In the proposed method, the Singular Value Decomposition (SVD)-based factorized spectral amplitude exponent β for each kernel component is adaptively calculated for effective bSAbased auditory filtering performance during kernel backfitting. Using a back-fitting threshold, the kernel backfitting process can automatically be iteratively performed until convergence. Experimental results show that the proposed method achieves better separation performance than GW based on kernel additive modeling.

/pdf/singing-voice-separation-from-monaural-music-based-on-kernel-2tz7z6r772.pdf

Singing Voice Separation from Monaural Music Based on Kernel Back-Fitting Using Beta-Order Spectral Amplitude Estimation.

Recently, kernel additive modeling with generalized spatial Wiener filtering (GW) was presented for music/voice separation. In this paper, an adaptive auditory filtering, called generalized weighted β-order MMSE estimation (WbE), is applied to the basic iterative kernel back-fitting algorithm for improving the separation performance of monaural music signal into music/voice components. In the proposed method, the perceptually weighting factor α and the singular value decomposition (SVD)-based factorized spectral amplitude exponent β for each kernel component are adaptively calculated for effective WbE-based auditory filtering performance. Experimental results show that the proposed method achieves better separation performance than GW and the existing Bayesian estimators.

Hye-Seung Cho

Papers

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

A robust audio identification for enhancing audio-based indoor localization

TV Advertisement Search Based on Audio Peak-Pair Hashing in Real Environments

Singing Voice Separation from Monaural Music Based on Kernel Back-Fitting Using Beta-Order Spectral Amplitude Estimation.

Vocal separation from monaural music using adaptive auditory filtering based on kernel back-fitting.