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  1. Article ; Online: Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey.

    Berwo, Michael Abebe / Khan, Asad / Fang, Yong / Fahim, Hamza / Javaid, Shumaila / Mahmood, Jabar / Abideen, Zain Ul / M S, Syam

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 10

    Abstract: Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid ... ...

    Abstract Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.
    Language English
    Publishing date 2023-05-17
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23104832
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Kiñit classification in Ethiopian chants, Azmaris and modern music: A new dataset and CNN benchmark.

    Retta, Ephrem Afele / Sutcliffe, Richard / Almekhlafi, Eiad / Enku, Yosef Kefyalew / Alemu, Eyob / Gemechu, Tigist Demssice / Berwo, Michael Abebe / Mhamed, Mustafa / Feng, Jun

    PloS one

    2023  Volume 18, Issue 4, Page(s) e0284560

    Abstract: In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and ... ...

    Abstract In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five expert judges into one of four well-known Ethiopian Kiñits, Tizita, Bati, Ambassel and Anchihoye. Each Kiñit uses its own pentatonic scale and also has its own stylistic characteristics. Thus, Kiñit classification needs to combine scale identification with genre recognition. After describing the dataset, we present the Ethio Kiñits Model (EKM), based on VGG, for classifying the EMIR clips. In Experiment 1, we investigated whether Filterbank, Mel-spectrogram, Chroma, or Mel-frequency Cepstral coefficient (MFCC) features work best for Kiñit classification using EKM. MFCC was found to be superior and was therefore adopted for Experiment 2, where the performance of EKM models using MFCC was compared using three different audio sample lengths. 3s length gave the best results. In Experiment 3, EKM and four existing models were compared on the EMIR dataset: AlexNet, ResNet50, VGG16 and LSTM. EKM was found to have the best accuracy (95.00%) as well as the fastest training time. However, the performance of VGG16 (93.00%) was found not to be significantly worse (P < 0.01). We hope this work will encourage others to explore Ethiopian music and to experiment with other models for Kiñit classification.
    MeSH term(s) Humans ; Benchmarking/classification ; Ethiopia ; Music ; Singing ; Datasets as Topic/classification
    Language English
    Publishing date 2023-04-20
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0284560
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Cross-Corpus Multilingual Speech Emotion Recognition

    Retta, Ephrem Afele / Sutcliffe, Richard / Mahmood, Jabar / Berwo, Michael Abebe / Almekhlafi, Eiad / Khan, Sajjad Ahmed / Chaudhry, Shehzad Ashraf / Mhamed, Mustafa / Feng, Jun

    Amharic vs. Other Languages

    2023  

    Abstract: In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used ... ...

    Abstract In a conventional Speech emotion recognition (SER) task, a classifier for a given language is trained on a pre-existing dataset for that same language. However, where training data for a language does not exist, data from other languages can be used instead. We experiment with cross-lingual and multilingual SER, working with Amharic, English, German and URDU. For Amharic, we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets. We followed previous research in mapping labels for all datasets to just two classes, positive and negative. Thus we can compare performance on different languages directly, and combine languages for training and testing. In Experiment 1, monolingual SER trials were carried out using three classifiers, AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for the three models were very similar for ASED and RAVDESS, suggesting that Amharic and English SER are equally difficult. Similarly, German SER is more difficult, and Urdu SER is easier. In Experiment 2, we trained on one language and tested on another, in both directions for each pair: Amharic<->German, Amharic<->English, and Amharic<->Urdu. Results with Amharic as target suggested that using English or German as source will give the best result. In Experiment 3, we trained on several non-Amharic languages and then tested on Amharic. The best accuracy obtained was several percent greater than the best accuracy in Experiment 2, suggesting that a better result can be obtained when using two or three non-Amharic languages for training than when using just one non-Amharic language. Overall, the results suggest that cross-lingual and multilingual training can be an effective strategy for training a SER classifier when resources for a language are scarce.

    Comment: 16 pages, 9 tables, 5 figures
    Keywords Computer Science - Computation and Language ; Computer Science - Neural and Evolutionary Computing ; Computer Science - Sound ; Electrical Engineering and Systems Science - Audio and Speech Processing
    Subject code 430
    Publishing date 2023-07-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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