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27 results on '"Iroro Orife"'

1. A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation

Author

Karn N. Watcharasupat, Chih-Wei Wu, Yiwei Ding, Iroro Orife, Aaron J. Hipple, Phillip A. Williams, Scott Kramer, Alexander Lerch, and William Wolcott

Subjects
Cinematic audio, deep learning, psychoacoustical frequency scale, source separation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Cinematic audio source separation is a relatively new subtask of audio source separation, with the aim of extracting the dialogue, music, and effects stems from their mixture. In this work, we developed a model generalizing the Bandsplit RNN for any complete or overcomplete partitions of the frequency axis. Psychoacoustically motivated frequency scales were used to inform the band definitions which are now defined with redundancy for more reliable feature extraction. A loss function motivated by the signal-to-noise ratio and the sparsity-promoting property of the 1-norm was proposed. We additionally exploit the information-sharing property of a common-encoder setup to reduce computational complexity during both training and inference, improve separation performance for hard-to-generalize classes of sounds, and allow flexibility during inference time with detachable decoders. Our best model sets the state of the art on the Divide and Remaster dataset with performance above the ideal ratio mask for the dialogue stem.

Published
2024
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2. A large TV dataset for speech and music activity detection

Author

Yun-Ning Hung, Chih-Wei Wu, Iroro Orife, Aaron Hipple, William Wolcott, and Alexander Lerch

Subjects
Speech and music activation detection, Dataset, Production TV audio, Acoustics. Sound, QC221-246, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract Automatic speech and music activity detection (SMAD) is an enabling task that can help segment, index, and pre-process audio content in radio broadcast and TV programs. However, due to copyright concerns and the cost of manual annotation, the limited availability of diverse and sizeable datasets hinders the progress of state-of-the-art (SOTA) data-driven approaches. We address this challenge by presenting a large-scale dataset containing Mel spectrogram, VGGish, and MFCCs features extracted from around 1600 h of professionally produced audio tracks and their corresponding noisy labels indicating the approximate location of speech and music segments. The labels are several sources such as subtitles and cuesheet. A test set curated by human annotators is also included as a subset for evaluation. To validate the generalizability of the proposed dataset, we conduct several experiments comparing various model architectures and their variants under different conditions. The results suggest that our proposed dataset is able to serve as a reliable training resource and leads to SOTA performances on various public datasets. To the best of our knowledge, this dataset is the first large-scale, open-sourced dataset that contains features extracted from professionally produced audio tracks and their corresponding frame-level speech and music annotations.

Published
2022
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3. Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

Author

Julia Kreutzer, Isaac Caswell, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Ortiz Suarez, Iroro Orife, Kelechi Ogueji, Andre Niyongabo Rubungo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, and Mofetoluwa Adeyemi

Subjects
Computational linguistics. Natural language processing, P98-98.5
Published
2023
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4. MasakhaNER: Named Entity Recognition for African Languages

Author

David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, and Salomey Osei

Subjects
Computational linguistics. Natural language processing, P98-98.5
Abstract

AbstractWe take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

Published
2021
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14. Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages.

Author

Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, Timi E. Fasubaa, Taiwo Fagbohungbe, Solomon Oluwole Akinola, Shamsuddeen Hassan Muhammad, Salomon Kabongo Kabenamualu, Salomey Osei, Freshia Sackey, Rubungo Andre Niyongabo, Ricky Macharm, Perez Ogayo, Orevaoghene Ahia, Musie Meressa Berhe, Mofetoluwa Adeyemi, Masabata Mokgesi-Selinga, Lawrence Okegbemi, Laura Martinus, Kolawole Tajudeen, Kevin Degila, Kelechi Ogueji, Kathleen Siminyu, Julia Kreutzer, Jason Webster, Jamiil Toure Ali, Jade Z. Abbott, Iroro Orife, Ignatius Ezeani, Idris Abdulkabir Dangana, Herman Kamper, Hady Elsahar, Goodness Duru, Ghollah Kioko, Espoir Murhabazi, Elan Van Biljon, Daniel Whitenack, Christopher Onyefuluchi, Chris Chinenye Emezue, Bonaventure F. P. Dossou, Blessing K. Sibanda, Blessing Itoro Bassey, Ayodele Olabiyi, Arshath Ramkilowan, Alp öktem, Adewale Akinfaderin, and Abdallah Bashir

Published
2020
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16. Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets.

Author

Isaac Caswell, Julia Kreutzer, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Javier Ortiz Suárez, Iroro Orife, Kelechi Ogueji, Rubungo Andre Niyongabo, Toan Q. Nguyen, Mathias Müller 0002, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine çabuk Balli, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, and Mofetoluwa Adeyemi

Published
2021

19. Masakhane - Machine Translation For Africa.

Author

Iroro Orife, Julia Kreutzer, Blessing K. Sibanda, Daniel Whitenack, Kathleen Siminyu, Laura Martinus, Jamiil Toure Ali, Jade Z. Abbott, Vukosi Marivate, Salomon Kabongo, Musie Meressa, Espoir Murhabazi, Orevaoghene Ahia, Elan Van Biljon, Arshath Ramkilowan, Adewale Akinfaderin, Alp öktem, Wole Akin, Ghollah Kioko, Kevin Degila, Herman Kamper, Bonaventure Dossou, Chris Emezue, Kelechi Ogueji, and Abdallah Bashir

Published
2020

27. MasakhaNER: Named entity recognition for African languages

Author

Julia Kreutzer, Ayodele Awokoya, Ignatius Ezeani, Rubungo Andre Niyongabo, Happy Buzaaba, Adewale Akinfaderin, Samuel Oyerinde, Stephen Mayhew, Emmanuel Anebi, Mofetoluwa Adeyemi, Kelechi Ogueji, Abdoulaye Diallo, Seid Muhie Yimam, Jade Abbott, Joyce Nakatumba-Nabende, Victor Akinode, Blessing Sibanda, Catherine Gitau, Chester Palen-Michel, Shamsuddeen Hassan Muhammad, Degaga Wolde, Graham Neubig, Tendai Marengereke, Paul Rayson, Derguene Mbaye, Eric Peter Wairagala, Daniel D'souza, Tosin P. Adewumi, Jonathan Mukiibi, Chris Chinenye Emezue, David Ifeoluwa Adelani, Shruti Rijhwani, Iroro Orife, Verrah Otiende, Maurice Katusiime, Yvonne Wambui, Dibora Gebreyohannes, Kelechi Nwaike, Salomey Osei, Chiamaka Chukwuneke, Henok Tilaye, Deborah Nabagereka, Thierno Ibrahima Diop, Orevaoghene Ahia, Jesujoba O. Alabi, Sebastian Ruder, Davis David, Mouhamadane Mboup, Samba Ngom, Tajuddeen R. Gwadabe, Bonaventure F. P. Dossou, Temilola Oloyede, Perez Ogayo, Clemencia Siro, Gerald Muriuki, Aremu Anuoluwapo, Nkiruka Odu, Tobius Saul Bateesa, Abdoulaye Faye, Israel Abebe Azime, Constantine Lignos, Saarland University [Saarbrücken], Masakhane NLP, Retro Rabbit, Carnegie Mellon University [Pittsburgh] (CMU), ProQuest, Google Research, Brandeis University, Université de Tsukuba = University of Tsukuba, DeepMind, DeepMind Technologies, Duolingo, African Institute for Mathematical Sciences (AIMS), University of Porto, Bayero University Kano (BUK), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Makerere University [Kampala, Ouganda] (MAK), African Leadership University, University of Lagos, Max Planck Institute for Informatics [Saarbrücken], Universität Hamburg (UHH), University of Chinese Academy of Sciences [Beijing] (UCAS), Lancaster University, University of Electronic Science and Technology of China (UESTC), United States International University - Africa, Niger-Volta Language Technologies Institute, Luleå University of Technology (LUT), African University of Science and Technology (AUST), University of Ibadan, Namibia University of Science and Technology (NUST), InstaDeep, Jacobs University [Bremen], University of Waterloo [Waterloo], European Project: 825081,H2020,COMPRISE(2018), Technical University of Munich (TUM), DeepMind [London], Universidade do Porto = University of Porto, and University of Electronic Science and Technology of China [Chengdu] (UESTC)

Subjects
FOS: Computer and information sciences, Linguistics and Language, Computer Science - Computation and Language, Computer science, business.industry, Computer Science - Artificial Intelligence, Communication, Languages of Africa, computer.software_genre, Code (semiotics), [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science Applications, Human-Computer Interaction, Artificial Intelligence (cs.AI), Named-entity recognition, Artificial Intelligence, Artificial intelligence, Transfer of learning, business, computer, Computation and Language (cs.CL), Natural language processing
Abstract

We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP., Comment: Accepted to TACL 2021, pre-MIT Press publication version

Published
2021
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