Amharic Music is an Ethiopian music streaming website that allows you to listen to Amharic songs by various Ethiopian artists. You can search for an amharic song by artist name, title or you can select an artist by clicking an artist name on the right.
In addition to the pentatonic scale, Amharic music also makes use of other scales and modes, such as the chromatic scale and the major and minor scales. These scales are often used in combination with the pentatonic scale to create a wide range of melodies and harmonies used in most modern and traditional Amharic music.
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There are many internationally recognized Ethiopian singers who have brought Amharic music to a global audience. Some of the most famous include Aster Aweke, Mahmoud Ahmed, Tewodros Kassahun (Teddy Afro), Aster Awoke, Ejigayehu Shibabaw (Gigi) and Tilahun Gessesse. These musicians have all made a significant impact on helping Amharic music to thrive and evolve making it one of the most recognized in the world.
We understand that music is an important part of Ethiopian culture, and that's why we have curated a collection of the best Ethiopian music from various genres and eras. Whether you're looking for classic Ethiopian jazz, traditional folk music, or the latest hits from Ethiopia's contemporary music scene, you'll find it all here.
At EphremTube, we take pride in providing a user-friendly experience for our visitors. Our website is easy to navigate and our music player is optimized for a seamless listening experience. You can enjoy your favorite Ethiopian music on our website from anywhere in the world.
When you listen mp3 musics , You can launch the player and it will be popped up .The benefit of popping up the player is that folks can navigate around the site, going from page to page, without interrupting the playback of each track. A pop up window keeps the player in a separate window and allows for uninterrupted playback.
Downloading and redistributing any media files, including audio, video, and images, from this site is forbidden. These files are intended for educational and entertainment purposes only and are encoded with medium quality to prevent unintended distribution.
hello my fellow ethiopian friend , as i am encouraging your effort for what you are trying to do ,i would like also to point out about the depth of your articles for example the history page if you say history you need to spend a lot of time to dig deep seaching for a good acount and authentic from the early history to the present day .you have to remember you are passing information that can tear or build the nation so please try to be precise and present a full history for what you are wrting . God bless you and God bless Ethiopia .
Jothilakshmi [9] applied a Gaussian mixture model (GMM), and a K-Nearest Neighbor (KNN) algorithm with spectral shape and perceptual features to Indian music datasets containing five genres. GMM gave the best accuracy with 91.25%. Rajesh [10] again utilized KNN and support vector machines (SVM), using different feature combinations. The highest classification recorded (96.05%) was by SVM using fractional MFCC with the addition of spectral roll off, flux, skewness, and kurtosis.
Al Mamun [11] used both deep learning and machine learning approaches on Bangla music datasets with six genres. The neural network model performed best compared to the machine learning methods, with accuracy 74%. Folorunso [12] investigated KNN, SVM, eXtreme Gradient Boosting (XGBoost) and Random Forest on Nigerian songs (in the ORIN dataset) with five genres. The XGBoost classifier had the highest accuracy (81.94%). De Sousa [13] implemented SVMs on a Brazilian Music Dataset (BMD) with seven genres. The set of features they proposed was specifically tailored to genre recognition yielding a high classification accuracy of 86.11%.
Kzrak [14] used Deep Belief Networks (DBNs) to classify the music genre of Turkish classical music Makams, working with seven Makam datasets. Mel Frequency Cepstral Coefficients (MFCC) were employed on the collection of features, resulting in a classification accuracy of 93.10%. Thomas and Alexander [15] used a parallel Convolutional Neural Network (CNN) to identify the mood and genre of a song. They employed Mel-Spectograms which were extracted from audio recordings, and applied a CNN to accomplish their desired task. Ali and Siddiqui [16] implemented a machine-learning algorithm to classify music genres, using KNN and SVM. To obtain information from individual songs they extracted MFCCs from audio files. Panteli et al. [17] used MFCC features and traditional machine learning to analyse recordings of world music from many countries with the aim of identifying those which are distinct. Phan et al. [18] carried out music classification in terms of environmental sound, audio scene and genre. They used four CRNN models, incorporating Mel, Gammatone, CQT and Raw inputs. The outputs were combined to produce the classification. Ma et al. [19] aimed to predict the genre of a film using Music Information Retrieval analysis. Various music features were used as input to several classifiers, including neural networks. MFCC and tonal features were found to be the best predictors of genre.
The Orthodox chants were collected from online sources such as YouTube and DireTube. Some Azmaris were specially recorded in Addis Ababa by an ethnomusicologist specialising in Azmari houses; these are traditional venues where Azmaris are studied and performed. Firstly, five typical Azmari houses were selected for the study. Secondly, these were visited on multiple occasions. Each time, a singer was asked whether they would record an Azmari of their choice which was in a specified Kiit. If the singer knew an Azmari in that Kiit and they agreed to the recording, it went ahead. Otherwise, another singer was asked. In this way, over several visits to each house, a collection of Azmaris in the different Kiits was built up.
VGG CNN-based models have performed very well for other music. Therefore, the proposed EKM architecture is based on VGG, as discussed earlier. Training and testing were performed with EMIR data using 3s samples. We extracted features using four methods, FilterBank with 40 bands, MelSpec with 128 bands, Chroma with 12 bands and MFCC with 40 bands. First, the model was trained and evaluated using just Filterbank features. Training and evaluation were then repeated using just MelSpec, Chroma and MFCC features.
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 Kiits, Tizita, Bati, Ambassel and Anchihoye. Each Kiit uses its own pentatonic scale and also has its own stylistic characteristics. Thus, Kiit classification needs to combine scale identification with genre recognition. After describing the dataset, we present the Ethio Kiits 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 Kiit 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 Kiit classification.
Ejigayehu Shibabaw, known by her stage name Gigi (born 1974), is an Ethiopian singer. She has performed the music of Ethiopia in combination with a wide variety of other genres, often in collaboration with her husband Bill Laswell, a bassist and producer.
Gigi recorded two albums for the expatriate Ethiopian community, but it was her 2001 album, titled simply Gigi, that brought her widespread attention. She had been noticed by Palm Pictures owner Chris Blackwell, who had years earlier introduced reggae to the mainstream through his former label, Island Records. Blackwell and Gigi's producer (and later, husband) Bill Laswell, decided to use American jazz musicians (including Herbie Hancock, Wayne Shorter, Pharoah Sanders, and others) to accompany Gigi on the album.
The result was a fusion of contemporary and traditional sounds. The album was a critical success internationally and generated controversy in her home country for such a radical break with Ethiopian popular music.[4] This release was soon followed by Illuminated Audio, an ambient dub style remix of the album by Laswell.
Music is an important part of everyday life. Around the world, it exists in many different forms and styles. Because musical preferences vary from person to person, categorizing music and making recommendations to listeners has become an important research topic [1] with many applications in listening apps and other platforms [2]. Multimedia file production and sharing through different mediums is increasing enormously. In consequence, indexing, browsing, and retrieval of music files has become challenging and time-consuming. Numerous digital music classification techniques have been introduced [3, 4], but the majority of them are only developed and tested on well-known Western music datasets. In Ethiopia, music classification is still being performed by individual music experts for archival or related purposes. Because of the amount of Ethiopian music now available in digital form, classification cannot be carried out with sufficient speed. As a result, even though the composer Saint Yared flourished in Ethiopia during the 6th Century [5] (p71), some five hundred years before Hildegard of Bingen [6], the music of this country is not well known elsewhere. In Ethiopia, music is based around several types of scale. Among these, four pentatonic scales (Kiits) are particularly important [7, 8]: Tizita, Bati, Ambassel, and Anchihoye. Because the music written in each Kiit has its own characteristic style and features, the task of Kiit classification is closely related to that of genre classification in European music. A major challenge for Ethiopian Kiit classification is the absence of training data. We have addressed this by creating the Ethiopian Music Information Retrieval (EMIR) dataset which includes data for the four main Kiits. We have also developed the Ethio Kiits Model (EKM), a genre classification model based on the well-known VGG architecture. We then carried out three experiments. The first experiment selected an appropriate method from the FilterBank, Mel-spectrogram (MelSpec), Chroma, and Mel-frequency Cepstral Coefficient (MFCC) technologies for extracting features from recordings in our EMIR dataset. MFCC was found to be the most effective in terms of accuracy and training time. The second experiment measured the effectiveness of different sample lengths for genre classification, in order to find the optimal length. The third experiment compared the classification performance of EKM and four other popular models using MFCC features, working with EMIR datasets. 2351a5e196
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