AUDIO SIGNAL PROCESSING SYSTEM UTILIZING AI MODELS

Abstract

ABSTRACT Audio Signal Processing System Utilizing AI Models The present disclosure introduces an audio signal processing system utilizing AI models 100, designed to enhance, personalize, and secure audio signals across various applications. The system features an audio input module 102 that captures and formats audio for processing, while an AI processing engine 104 performs advanced noise reduction, speech enhancement, and sound personalization. An environmental analysis algorithm 108 continuously monitors ambient noise, providing dynamic feedback, and a predictive audio processing module 120 preemptively adjusts settings based on anticipated conditions. Assistive device integration module 110 connects seamlessly with hearing aids, ensuring enhanced audio tailored to individual needs. Privacy and security framework 114 protects data through encryption and user-controlled permissions, while model optimization and deployment module 116 allows efficient processing on resource-limited devices. The user interface and personalization app 118 enables users to customize audio preferences, creating responsive audio experience. Reference Fig 1

Specification

Description:DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of audio signal processing system utilising AI models and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, audio signal processing system utilising AI models 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of audio input module 102, AI processing engine 104, audio output module 106, environmental analysis algorithm 108, assistive device integration module 110, smart city and IoT integration interface 112, privacy and security framework 114, model optimization and deployment module 116, user interface and personalization app 118, predictive audio processing module 120, self-learning and model adaptation module 122, sentiment analysis subsystem 124, biometric integration module 126 and data compression module 128.

[00029] Referring to Fig. 1, the present disclosure provides details of an audio signal processing system utilizing AI models 100 for real-time enhancement, noise reduction, and personalized audio output. The system improves sound quality in various environments, adapting dynamically to user preferences and environmental conditions. In one of the embodiments, the audio signal processing system includes key components such as audio input module 102, AI processing engine 104, and audio output module 106, enabling efficient signal acquisition, processing, and output. The system incorporates environmental analysis algorithm 108 and predictive audio processing module 120 to dynamically adjust audio settings based on ambient noise and expected conditions. Additional components like biometric integration module 126 and user interface and personalization app 118 offer custom audio experiences and bio-responsive adjustments. For secure operation, privacy and security framework 114 ensures data privacy and controlled sharing, while model optimization and deployment module 116 supports functionality on resource-constrained devices.

[00030] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with audio input module 102, which captures raw audio signals from various input devices like microphones, headsets, or other audio equipment. This module processes the initial input to ensure compatibility with the AI processing engine 104, preparing it for further enhancement and analysis. The audio input module 102 can handle both real-time and recorded inputs, optimizing them for adaptive processing in dynamic environments. It works closely with environmental analysis algorithm 108 to analyze ambient noise levels and adjust input configurations as needed, ensuring high-quality, adaptable audio processing.

[00031] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with AI processing engine 104, which is the central processing component that utilizes machine learning models to enhance, separate, and personalize audio. This engine includes two critical subsystems: noise reduction and speech enhancement and adaptive sound personalization, enabling real-time clarity and customization. It continuously interacts with environmental analysis algorithm 108 to dynamically adjust settings based on external conditions, like background noise levels. The AI processing engine 104 also interfaces with predictive audio processing module 120 to preemptively adjust audio characteristics, improving user experience across various environments.

[00032] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with audio output module 106, responsible for delivering the processed audio in formats compatible with target devices, including mobile phones, hearing aids, and public address systems. This module ensures that audio output aligns with the specific requirements of different applications, such as low latency for real-time communication or high-quality streams for media production. The audio output module 106 interacts closely with the AI processing engine 104 to adjust output based on processed enhancements, while also leveraging feedback from user interface and personalization app 118 for personalized sound delivery.

[00033] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with environmental analysis algorithm 108, which continuously monitors audio surroundings to identify noise patterns and adapt audio processing parameters accordingly. This algorithm works in tandem with AI processing engine 104 to adjust noise reduction levels in real-time, maintaining clarity despite environmental shifts. Additionally, it collaborates with predictive audio processing module 120 to anticipate changes in ambient noise and make preemptive adjustments. This capability ensures the system remains responsive and adaptable, providing consistent audio quality without manual intervention.

[00034] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with assistive device integration module 110, designed for seamless connectivity with assistive devices like hearing aids and headphones. This module facilitates wireless data exchange, allowing real-time audio enhancement specifically tailored for users with hearing impairments. It collaborates with user interface and personalization app 118 to enable users to adjust parameters such as noise suppression level and speech clarity enhancement. The assistive device integration module 110 ensures that processed audio is optimized for individual hearing needs, contributing to a customized, accessible user experience.

[00035] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with smart city and iot integration interface 112, allowing integration with IoT frameworks and public address systems in urban environments. This component provides intelligent audio adjustments for emergency notifications and other public announcements, ensuring clarity even in noisy surroundings. The smart city and iot integration interface 112 works closely with environmental analysis algorithm 108 to adapt audio processing to the specific acoustic requirements of each environment. Additionally, it collaborates with privacy and security framework 114 to safeguard data in public infrastructure applications.

[00036] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with privacy and security framework 114, ensuring that all audio data transmission is encrypted and user privacy settings are maintained. This framework allows users to set data-sharing preferences and provides controlled access to sensitive audio information, making it suitable for healthcare, legal, and public safety applications. The privacy and security framework 114 interacts with assistive device integration module 110 and smart city and iot integration interface 112 to maintain data security across devices and public systems.

[00037] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with model optimization and deployment module 116, which optimizes AI models to function efficiently on resource-limited devices, such as mobile phones and wearables. This module minimizes computational demands without compromising audio quality, making the system suitable for portable applications. It integrates closely with AI processing engine 104 to adjust model complexity based on device capabilities, ensuring that audio enhancement features are accessible across a range of platforms, from personal devices to larger infrastructure setups.
[00038] Referring to Fig. 1, audio signal processing system utilizing AI models is provided with user interface and personalization app 118, enabling users to customize their audio settings, such as noise suppression levels, equalization, and sound preferences. This app provides real-time control over the audio output, allowing users to tailor their experience based on specific needs or environments. The user interface and personalization app 118 communicates directly with the AI processing engine 104 and audio output module 106 to apply user-defined adjustments instantly, enhancing the personalized sound experience.

[00039] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with predictive audio processing module 120, which uses algorithms to anticipate environmental conditions, such as noise levels, and adjust audio settings in advance. This module is particularly useful for applications in dynamic settings, like outdoor events or transportation hubs. The predictive audio processing module 120 collaborates with environmental analysis algorithm 108 and AI processing engine 104 to preemptively modify audio output, ensuring clarity and quality in rapidly changing environments.

[00040] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with self-learning and model adaptation module 122, which enables the AI models to progressively learn and refine audio processing based on user interactions and environmental patterns. This component allows the system to adapt to individual preferences over time, enhancing the personalization of audio output. The self-learning and model adaptation module 122 works with AI processing engine 104 to continuously improve noise reduction and speech enhancement settings. It also utilizes data from environmental analysis algorithm 108 to identify recurring patterns, making the system more efficient with each use.

[00041] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with sentiment analysis subsystem 124, which detects the emotional tone and sentiment in speech, enabling context-aware audio adjustments. This component enhances interactions in applications where sentiment is important, such as customer service and emergency response. The sentiment analysis subsystem 124 collaborates with AI processing engine 104 to adjust audio features, such as tone and clarity, to match the detected emotional context. By interacting with predictive audio processing module 120, it can preemptively modify settings based on anticipated emotional cues, creating a more immersive experience.

[00042] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with biometric integration module 126, allowing it to connect with biometric devices, such as wearables, to receive real-time physiological feedback, like stress levels or concentration. This bio-responsive component enables personalized audio adjustments based on the user's physical state, enhancing comfort and focus. The biometric integration module 126 works with the user interface and personalization app 118, allowing users to set preferences for bio-responsive adjustments. It interacts with AI processing engine 104 to dynamically modify audio output based on biometric signals, making it particularly useful in healthcare and training applications.

[00043] Referring to Fig. 1, audio signal processing system utilizing AI models 100 is provided with data compression module 128, which uses advanced AI-driven algorithms to compress audio data without compromising quality, enabling efficient transmission over low-bandwidth networks. This component is essential for applications in remote locations, telemedicine, and emergency communications, where bandwidth may be limited. The data compression module 128 works closely with model optimization and deployment module 116 to ensure compression efficiency on resource-constrained devices. It also collaborates with privacy and security framework 114 to maintain data security during transmission, making it suitable for sensitive and mission-critical communications.


[00044] Referring to Fig 2, there is illustrated method 200 for audio signal processing system utilizing AI models 100. The method comprises:
At step 202, method 200 includes receiving an audio signal through the audio input module 102, where initial processing formats the raw audio for further analysis;
At step 204, method 200 includes the audio input module 102 sending the formatted audio to the AI processing engine 104 for noise reduction and speech enhancement using trained AI models;
At step 206, method 200 includes the environmental analysis algorithm 108 continuously monitoring surrounding noise patterns and providing feedback to the AI processing engine 104 to dynamically adjust audio processing parameters in real time;
At step 208, method 200 includes the predictive audio processing module 120 preemptively adjusting audio settings based on anticipated environmental conditions, enhancing audio clarity ahead of expected noise changes;
At step 210, method 200 includes the AI processing engine 104 applying adaptive sound personalization based on user preferences received through the user interface and personalization app 118, tailoring the audio output to individual needs;
At step 212, method 200 includes the AI processing engine 104 processing the audio based on real-time input from the biometric integration module 126, which adjusts audio characteristics in response to physiological feedback, like stress or concentration levels;
At step 214, method 200 includes the processed audio being outputted through the audio output module 106 in a compatible format for target devices, such as mobile phones, hearing aids, or public announcement systems;
At step 216, method 200 includes the audio signal being compressed by the data compression module 128 if it is being transmitted over low-bandwidth networks, ensuring high-quality transmission without compromising clarity;
At step 218, method 200 includes the privacy and security framework 114 encrypting the audio data before transmission, safeguarding user privacy in applications such as healthcare or public safety.
[00045] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00046] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00047] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A audio signal processing system utilizing AI models 100 comprising of
audio input module 102 to capture and format raw audio signals for processing;
AI processing engine 104 to perform noise reduction, speech enhancement, and sound personalization using AI models;
audio output module 106 to deliver processed audio in formats compatible with various devices;
environmental analysis algorithm 108 to monitor ambient noise and adjust audio processing in real-time;
assistive device integration module 110 to connect seamlessly with hearing aids and other assistive listening devices;
smart city and iot integration interface 112 to support applications in public address systems and urban infrastructure;
privacy and security framework 114 to secure audio data transmission and ensure user privacy;
model optimization and deployment module 116 to optimize AI models for efficient performance on resource-limited devices;
user interface and personalization app 118 to enable users to adjust audio settings and customize their listening experience;
predictive audio processing module 120 to preemptively adjust audio settings based on anticipated environmental changes;
self-learning and model adaptation module 122 to progressively enhance audio processing through adaptive learning;
sentiment analysis subsystem 124 to detect emotional tone and adjust audio settings accordingly;
biometric integration module 126 to adjust audio characteristics based on real-time physiological feedback; and
data compression module 128 to reduce audio data size for transmission over low-bandwidth networks.
2. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the audio input module 102 is configured to capture and format audio signals in real-time, preparing raw audio for efficient processing by dynamically adjusting input parameters based on ambient noise and device compatibility.

3. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the AI processing engine 104 is configured to apply advanced machine learning models for noise reduction and speech enhancement, dynamically adapting to varying audio conditions and personalizing output based on user preferences.

4. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the environmental analysis algorithm 108 is configured to continuously monitor surrounding audio conditions and provide real-time feedback to the AI processing engine 104, enabling automatic adjustments to noise suppression levels based on detected changes in ambient sound.

5. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the predictive audio processing module 120 is configured to anticipate environmental conditions using contextual data, allowing preemptive adjustment of audio settings to enhance clarity and minimize noise disruptions before they occur.

6. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the assistive device integration module 110 is configured to facilitate real-time connectivity with hearing aids and other assistive devices, delivering customized audio output that enhances speech intelligibility and reduces background noise specific to the hearing capabilities of individual users.

7. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the model optimization and deployment module 116 is configured to enable efficient AI processing on resource-constrained devices, utilizing model compression techniques to maintain high-quality audio enhancement on mobile and wearable devices.

8. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the privacy and security framework 114 is configured to protect audio data during transmission through encryption protocols and user-controlled data permissions, ensuring secure and private processing suitable for applications in healthcare and public safety.

9. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the user interface and personalization app 118 is configured to allow users to customize audio settings, such as noise suppression and equalization, in real-time, providing an adaptive audio experience that responds to individual preferences and environmental conditions.

10. The audio signal processing system utilizing AI models 100 as claimed in claim 1, wherein the method comprises of
sensor suite 102 receiving an audio signal through the audio input module 102, where initial processing formats the raw audio for further analysis;
audio input module 102 sending the formatted audio to the AI processing engine 104 for noise reduction and speech enhancement using trained AI models;
environmental analysis algorithm 108 continuously monitoring surrounding noise patterns and providing feedback to the AI processing engine 104 to dynamically adjust audio processing parameters in real time;
predictive audio processing module 120 preemptively adjusting audio settings based on anticipated environmental conditions, enhancing audio clarity ahead of expected noise changes;
AI processing engine 104 applying adaptive sound personalization based on user preferences received through the user interface and personalization app 118, tailoring the audio output to individual needs;
AI processing engine 104 processing the audio based on real-time input from the biometric integration module 126, which adjusts audio characteristics in response to physiological feedback, like stress or concentration levels;
processed audio being outputted through the audio output module 106 in a compatible format for target devices, such as mobile phones, hearing aids, or public announcement systems;
audio signal being compressed by the data compression module 128 if it is being transmitted over low-bandwidth networks, ensuring high-quality transmission without compromising clarity; and
privacy and security framework 114 encrypting the audio data before transmission, safeguarding user privacy in applications such as healthcare or public safety.

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