by jsendak | Nov 1, 2024 | Computer Science
arXiv:2410.23325v1 Announce Type: cross
Abstract: Vocal education in the music field is difficult to quantify due to the individual differences in singers’ voices and the different quantitative criteria of singing techniques. Deep learning has great potential to be applied in music education due to its efficiency to handle complex data and perform quantitative analysis. However, accurate evaluations with limited samples over rare vocal types, such as Mezzo-soprano, requires extensive well-annotated data support using deep learning models. In order to attain the objective, we perform transfer learning by employing deep learning models pre-trained on the ImageNet and Urbansound8k datasets for the improvement on the precision of vocal technique evaluation. Furthermore, we tackle the problem of the lack of samples by constructing a dedicated dataset, the Mezzo-soprano Vocal Set (MVS), for vocal technique assessment. Our experimental results indicate that transfer learning increases the overall accuracy (OAcc) of all models by an average of 8.3%, with the highest accuracy at 94.2%. We not only provide a novel approach to evaluating Mezzo-soprano vocal techniques but also introduce a new quantitative assessment method for music education.
Deep Learning in Vocal Education: A Novel Approach to Evaluating Mezzo-soprano Vocal Techniques
Vocal education in the music field has always been a challenging endeavor, primarily due to the individual differences in singers’ voices and the subjective nature of evaluating singing techniques. However, recent advancements in deep learning offer an exciting opportunity to revolutionize music education by providing a quantitative analysis of vocal techniques. In this article, we explore the application of deep learning models in vocal technique evaluation and introduce a new method for assessing Mezzo-soprano vocal techniques.
One of the key advantages of deep learning is its ability to handle complex data and extract meaningful patterns from it. By leveraging this capability, we can train deep learning models on a diverse range of vocal samples, allowing them to learn the intricate nuances and subtleties of Mezzo-soprano singing. To achieve this, we employ transfer learning, a technique that utilizes pre-trained models on large datasets such as ImageNet and Urbansound8k.
Transfer learning enables us to fine-tune the pre-trained models to specialize in evaluating Mezzo-soprano vocal techniques. By retraining the models on a dedicated dataset called the Mezzo-soprano Vocal Set (MVS), we address the challenge of limited samples for rare vocal types. The MVS contains carefully annotated vocal recordings of Mezzo-soprano singers, providing a rich source of training data for our deep learning models.
Our experimental results demonstrate the effectiveness of transfer learning in improving the precision of vocal technique evaluation. We observed an average increase of 8.3% in the overall accuracy (OAcc) of all models, with the highest accuracy reaching an impressive 94.2%. These findings highlight the potential of deep learning to enhance vocal education by offering a quantitative and objective assessment of Mezzo-soprano vocal techniques.
This research aligns with the broader field of multimedia information systems, where the integration of various disciplines is essential for developing innovative solutions. The concepts explored in this study draw upon the fields of deep learning, where neural networks are trained on large datasets, and vocal education, where subjective assessments are traditionally used. By combining these disciplines, we create a multidisciplinary approach that bridges the gap between quantitative analysis and artistic expression.
Furthermore, this work has implications for other domains such as animations, artificial reality, augmented reality, and virtual realities, where realistic and expressive virtual characters are essential. The use of deep learning models for vocal technique evaluation can contribute to the development of more realistic and human-like virtual characters, enhancing the immersive experience in these virtual environments.
In conclusion, the application of deep learning in vocal education, particularly in evaluating Mezzo-soprano vocal techniques, offers promising avenues for advancing music education. By leveraging transfer learning and constructing dedicated datasets, we can improve the precision of vocal technique assessment and introduce a new quantitative assessment method. This research not only expands our understanding of deep learning but also demonstrates its potential to transform the field of music education and its interconnectedness with multimedia information systems.
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by jsendak | Nov 1, 2024 | Computer Science
The article discusses the importance of trajectory prediction in autonomous driving systems and introduces a novel scheme called AiGem (Agent-Interaction Graph Embedding) for predicting traffic vehicle trajectories.
Overview of AiGem
AiGem follows a four-step approach to predict trajectories:
- Formulating the Graph: AiGem represents historical traffic interactions as a graph. At each time step, spatial edges are created between the agents, and the spatial graphs are connected in chronological order using temporal edges.
- Generating Graph Embeddings: AiGem applies a depthwise graph encoder network to the spatial-temporal graph to generate graph embeddings. These embeddings capture the representation of all nodes (agents) in the graph.
- Decoding States: The graph embeddings of the current timestamp are used by a sequential Gated Recurrent Unit decoder network to obtain decoded states.
- Trajectory Prediction: The decoded states serve as inputs to an output network consisting of a Multilayer Perceptron, which predicts the trajectories.
Advantages of AiGem
According to the results, AiGem outperforms state-of-the-art deep learning algorithms for longer prediction horizons. This suggests that AiGem is capable of accurately predicting traffic vehicle trajectories for extended periods of time.
Expert Analysis
AiGem introduces an innovative approach to trajectory prediction by leveraging graph embedding techniques. By representing traffic interactions as a graph and using a depthwise graph encoder network, AiGem captures the spatial and temporal relationships between agents. This enables the system to learn and predict complex trajectories in a more accurate manner.
The sequential Gated Recurrent Unit decoder network further enhances the prediction process by leveraging the decoded states from the graph embeddings. This sequential information helps capture the dynamics and evolution of the traffic scenario, leading to more accurate trajectory predictions.
The use of a Multilayer Perceptron in the output network allows for efficient mapping of the decoded states to the predicted trajectories. The MLP can capture non-linear relationships, enabling better trajectory predictions even over longer horizons.
AiGem’s superior performance compared to existing deep learning algorithms for longer prediction horizons suggests its potential to be integrated into real-world autonomous driving systems. By accurately predicting traffic vehicle trajectories, autonomous agents can make better decisions, leading to improved safety and efficiency on the roads.
Future Directions
While AiGem shows promising results, there are several avenues for future research and improvement. One potential direction is the exploration of alternative graph embedding techniques that may capture additional information or improve computational efficiency.
Furthermore, expanding the dataset used for training and evaluation could enhance the generalizability of AiGem. Including a wider range of traffic scenarios, road conditions, and driving styles can help the system adapt to various real-world driving environments.
Additionally, incorporating real-time sensor data from the autonomous car, such as lidar or camera inputs, could further refine trajectory predictions. By incorporating live data, the system can respond to dynamic changes in the environment and improve prediction accuracy.
In conclusion, AiGem presents a novel scheme for traffic vehicle trajectory prediction in autonomous driving systems. Its graph embedding approach, sequential decoding, and MLP-based trajectory prediction contribute to its superior performance. With further research and improvements, AiGem has the potential to enhance the safety and efficiency of autonomous driving systems.
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by jsendak | Oct 31, 2024 | Computer Science
arXiv:2410.22350v1 Announce Type: new
Abstract: In this paper, we propose a quality-aware end-to-end audio-visual neural speaker diarization framework, which comprises three key techniques. First, our audio-visual model takes both audio and visual features as inputs, utilizing a series of binary classification output layers to simultaneously identify the activities of all speakers. This end-to-end framework is meticulously designed to effectively handle situations of overlapping speech, providing accurate discrimination between speech and non-speech segments through the utilization of multi-modal information. Next, we employ a quality-aware audio-visual fusion structure to address signal quality issues for both audio degradations, such as noise, reverberation and other distortions, and video degradations, such as occlusions, off-screen speakers, or unreliable detection. Finally, a cross attention mechanism applied to multi-speaker embedding empowers the network to handle scenarios with varying numbers of speakers. Our experimental results, obtained from various data sets, demonstrate the robustness of our proposed techniques in diverse acoustic environments. Even in scenarios with severely degraded video quality, our system attains performance levels comparable to the best available audio-visual systems.
Expert Commentary: A Quality-Aware End-to-End Audio-Visual Neural Speaker Diarization Framework
This paper presents a novel approach to audio-visual speaker diarization, which is the process of determining who is speaking when in an audio or video recording. Speaker diarization is a crucial step in various multimedia information systems, such as video conferencing, surveillance systems, and automatic transcription services. This research proposes a quality-aware end-to-end framework that leverages both audio and visual information to accurately identify and separate individual speakers, even in challenging scenarios.
The proposed framework is multi-disciplinary in nature, combining concepts from audio processing, computer vision, and deep learning. By taking both audio and visual features as inputs, the model is able to capture a broader range of information, leading to more accurate speaker discrimination. This multi-modal approach allows the system to handle situations with overlapping speech, where audio-only methods may struggle.
One key aspect of this framework is the quality-aware audio-visual fusion structure. It addresses signal quality issues that commonly arise in real-world scenarios, such as noise, reverberation, occlusions, and unreliable detection. By incorporating quality-aware fusion, the system can mitigate the negative effects of audio and video degradations, leading to more robust performance. This is particularly important in applications where the video quality may be compromised, as the proposed framework can still perform at high levels.
Another notable contribution of this research is the use of a cross attention mechanism applied to multi-speaker embedding. This mechanism enables the network to handle scenarios with varying numbers of speakers. This is crucial in real-world scenarios where the number of speakers may change dynamically, such as meetings or group conversations.
The experimental results presented in the paper demonstrate the effectiveness and robustness of the proposed techniques. The framework achieves competitive performance on various datasets, even in situations with severely degraded video quality. These results highlight the potential of leveraging both audio and visual information for speaker diarization tasks.
In the wider field of multimedia information systems, this research contributes to the advancement of audio-visual processing techniques. By combining audio and visual cues, the proposed framework enhances the capabilities of multimedia systems, enabling more accurate and reliable speaker diarization. This has implications for various applications, including video surveillance, automatic transcription services, and virtual reality systems.
Furthermore, the concepts presented in this paper have connections to other related fields such as animations, artificial reality, augmented reality, and virtual realities. The use of audio-visual fusion and multi-modal information processing can be applied to enhance user experiences in these domains. For example, in virtual reality, accurate audio-visual synchronization and speaker separation can greatly enhance the immersion and realism of virtual environments, leading to more engaging experiences for users.
In conclusion, this paper introduces a quality-aware end-to-end audio-visual neural speaker diarization framework that leverages multi-modal information and addresses signal quality issues. The proposed techniques demonstrate robust performance in diverse acoustic environments, highlighting the potential of combining audio and visual cues for speaker diarization tasks. This research contributes to the wider field of multimedia information systems and has implications for various related domains, such as animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Oct 31, 2024 | Computer Science
Machine translation has made significant progress in recent years with advancements in Natural Language Processing (NLP) technology. This paper introduces a novel Seq2Seq model that aims to improve translation quality while reducing the storage space required by the model.
The proposed model utilizes a Bidirectional Long Short-Term Memory network (Bi-LSTM) as the encoder, which allows it to capture the context information of the input sequence effectively. This is an important aspect in ensuring accurate and high-quality translations. Additionally, the decoder incorporates an attention mechanism, which further enhances the model’s ability to focus on key information during the translation process. This attention mechanism is particularly useful in handling long or complex sentences.
One notable advantage of this model is its size. Compared to the current mainstream Transformer model, the proposed model achieves superior performance, while maintaining a smaller size. This is a critical factor in real-world applications, as smaller models require less computational resources and are more suitable for deployment in resource-constrained scenarios.
To validate the effectiveness of the model, a series of experiments were conducted. These experiments included assessing the model’s performance on different language pairs and comparing it with traditional Seq2Seq models. The results demonstrated that the proposed model not only maintained translation accuracy but also significantly reduced the storage requirements.
The reduction in storage requirements is of great significance, as it enables the model to be deployed on devices with limited memory capacity or in situations where internet connectivity is limited. This makes the model practical and versatile, opening up opportunities for translation applications in various resource-constrained scenarios.
In summary, this paper presents a novel Seq2Seq model that combines a Bi-LSTM encoder with an attention mechanism in the decoder. The model achieves superior performance on the WMT14 machine translation dataset while maintaining a smaller size compared to the mainstream Transformer model. The reduction in storage requirements is a significant advantage, making the model suitable for resource-constrained scenarios. Overall, this research contributes to the advancement of machine translation technology and has practical implications for real-world application.
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by jsendak | Oct 30, 2024 | Computer Science
arXiv:2410.22112v1 Announce Type: new
Abstract: This paper studies an efficient multimodal data communication scheme for video conferencing. In our considered system, a speaker gives a talk to the audiences, with talking head video and audio being transmitted. Since the speaker does not frequently change posture and high-fidelity transmission of audio (speech and music) is required, redundant visual video data exists and can be removed by generating the video from the audio. To this end, we propose a wave-to-video (Wav2Vid) system, an efficient video transmission framework that reduces transmitted data by generating talking head video from audio. In particular, full-duration audio and short-duration video data are synchronously transmitted through a wireless channel, with neural networks (NNs) extracting and encoding audio and video semantics. The receiver then combines the decoded audio and video data, as well as uses a generative adversarial network (GAN) based model to generate the lip movement videos of the speaker. Simulation results show that the proposed Wav2Vid system can reduce the amount of transmitted data by up to 83% while maintaining the perceptual quality of the generated conferencing video.
Analyzing an Efficient Multimodal Data Communication Scheme for Video Conferencing
The study presented in this paper focuses on the development of an efficient multimodal data communication scheme for video conferencing. In today’s world, video conferencing has become increasingly popular, and it is important to optimize the transmission of video and audio data to deliver a seamless and high-quality communication experience.
The research specifically looks into the scenario where a speaker is giving a talk to an audience through video conferencing. In such cases, the speaker’s posture does not significantly change, and the primary focus is on transmitting high-fidelity audio. Due to the relative stability of the speaker’s visual representation, there exists redundant visual video data that can be eliminated by generating the video from the audio signal.
This concept of generating video from audio is where the proposed wave-to-video (Wav2Vid) system comes into play. The Wav2Vid system is designed to efficiently transmit video data by extracting and encoding the audio and video semantics using neural networks (NNs). The video is generated by combining the decoded audio and video data at the receiver’s end, and a generative adversarial network (GAN) based model is used to generate accurate lip movement videos of the speaker.
The key advantage of the Wav2Vid system is its ability to significantly reduce the amount of transmitted data, up to 83%, while maintaining the perceptual quality of the generated conferencing video. This reduction in data transmission has implications for bandwidth usage, especially in situations where network resources might be limited or expensive.
The research presented in this paper is a prime example of the multi-disciplinary nature of multimedia information systems. It combines principles from signal processing, machine learning, and computer vision to develop an innovative solution for optimizing video conferencing. This approach highlights the importance of integrating various disciplines to address complex challenges in the field.
Furthermore, the concept of generating video from audio has implications beyond video conferencing. It can be applied to various multimedia applications such as animations, artificial reality, augmented reality, and virtual realities. By eliminating redundant visual data and generating visuals from audio signals, it opens up possibilities for efficient content generation and transmission in these domains.
In conclusion, the proposed Wav2Vid system presents an efficient multimodal data communication scheme for video conferencing. Its ability to reduce data transmission while maintaining perceptual quality is a valuable contribution to the field. The research also demonstrates the interdisciplinary nature of multimedia information systems and highlights the potential applications of generating visuals from audio signals in various multimedia domains.
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by jsendak | Oct 30, 2024 | Computer Science
With the advancements in quantum computing, researchers have been focusing on using quantum algorithms to solve combinatorial optimization problems. One of the key models used in this area is the Quadratic Unconstrained Binary Optimization (QUBO) model, which acts as a connection between quantum computers and combinatorial optimization problems. However, there has been a lack of research on QUBO modeling for variant problems related to the Dominating Problem (DP).
The Dominating Problem, also known as the Domination Problem, has applications in various real-world scenarios such as the fire station problem and social network theory. It has several variants, including independent DP, total DP, and k-domination. Despite its importance, there has been a scarcity of quantum computing research on these variant problems.
In this paper, the researchers aim to fill this research gap by investigating QUBO modeling methods for the classic DP and its variants. They propose a QUBO modeling method for the classic DP that can utilize fewer qubits compared to previous studies. This is a significant development as it lowers the barrier for solving DP on quantum computers, making it more accessible and feasible.
Furthermore, the researchers provide QUBO modeling methods for the first time for many variants of DP problems. This expansion of QUBO modeling techniques will contribute to the acceleration of DP’s entry into the quantum era. By providing methods for solving these variant problems on quantum computers, researchers can explore new possibilities and applications in combinatorial optimization.
Overall, this paper contributes to the field of quantum computing by addressing the lack of research on QUBO modeling for variant problems related to the Dominating Problem. The proposed QUBO modeling methods not only optimize the use of qubits for solving the classic DP but also provide new avenues for solving variant problems on quantum computers. This research opens up opportunities for further exploration and advancement in the field of combinatorial optimization on quantum platforms.
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