arXiv subjects

Mohammad Reza Hasanabadi

Publications and source records attributed to Mohammad Reza Hasanabadi.

3 recordsLinked to original sources

arXiv2023

A Novel Approach for Object Based Audio Broadcasting

Object Based Audio (OBA) provides a new kind of audio experience, delivered to the audience to personalize and customize their experience of listening and to give them choice of what and how to hear their audio content. OBA can be applied to different platforms such as broadcasting, streaming and cinema sound. This paper presents a novel approach for creating object-based audio on the production side. The approach here presents Sample-by-Sample Object Based Audio (SSOBA) embedding. SSOBA places audio object samples in such a way that allows audiences to easily individualize their chosen audio sources according to their interests and needs. SSOBA is an extra service and not an alternative, so it is also compliant with legacy audio players. The biggest advantage of SSOBA is that it does not require any special additional hardware in the broadcasting chain and it is therefore easy to implement and equip legacy players and decoders with enhanced ability. Input audio objects, number of output channels and sampling rates are three important factors affecting SSOBA performance and specifying it to be lossless or lossy. SSOBA adopts interpolation at the decoder side to compensate for eliminated samples. Both subjective and objective experiments are carried out to evaluate the output results at each step. MUSHRA subjective experiments conducted after the encoding step shows good-quality performance of SSOBA with up to five objects. SNR measurements and objective experiments, performed after decoding and interpolation, show significant successful recovery and separation of audio objects. Experimental results show that a minimum sampling rate of 96 kHz is indicated to encode up to five objects in a Stereo-mode channel to acquire good subjective and objective results simultaneously.

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arXiv2023

MFCC-GAN Codec: A New AI-based Audio Coding

In this paper, we proposed AI-based audio coding using MFCC features in an adversarial setting. We combined a conventional encoder with an adversarial learning decoder to better reconstruct the original waveform. Since GAN gives implicit density estimation, therefore, such models are less prone to overfitting. We compared our work with five well-known codecs namely AAC, AC3, Opus, Vorbis, and Speex, performing on bitrates from 2kbps to 128kbps. MFCCGAN_36k achieved the state-of-the-art result in terms of SNR despite a lower bitrate in comparison to AC3_128k, AAC_112k, Vorbis_48k, Opus_48k, and Speex_48K. On the other hand, MFCCGAN_13k also achieved high SNR=27 which is equal to that of AC3_128k, and AAC_112k while having a significantly lower bitrate (13 kbps). MFCCGAN_36k achieved higher NISQA-MOS results compared to AAC_48k while having a 20% lower bitrate. Furthermore, MFCCGAN_13k obtained NISQAMOS= 3.9 which is much higher than AAC_24k, AAC_32k, AC3_32k, and AAC_48k. For future work, we finally suggest adopting loss functions optimizing intelligibility and perceptual metrics in the MFCCGAN structure to improve quality and intelligibility simultaneously.

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arXiv2023

An overview of text-to-speech systems and media applications

Producing synthetic voice, similar to human-like sound, is an emerging novelty of modern interactive media systems. Text-To-Speech (TTS) systems try to generate synthetic and authentic voices via text input. Besides, well known and familiar dubbing, announcing and narrating voices, as valuable possessions of any media organization, can be kept forever by utilizing TTS and Voice Conversion (VC) algorithms . The emergence of deep learning approaches has made such TTS systems more accurate and accessible. To understand TTS systems better, this paper investigates the key components of such systems including text analysis, acoustic modelling and vocoding. The paper then provides details of important state-of-the-art TTS systems based on deep learning. Finally, a comparison is made between recently released systems in term of backbone architecture, type of input and conversion, vocoder used and subjective assessment (MOS). Accordingly, Tacotron 2, Transformer TTS, WaveNet and FastSpeech 1 are among the most successful TTS systems ever released. In the discussion section, some suggestions are made to develop a TTS system with regard to the intended application.

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