NEXT-GEN SOUND PROCESSING SOLUTIONS FOR SENTIMENT ANALYSIS

User requirements analysis

Conducted thorough requirements gathering and analysis, involving input from both the client and end users, to gain a deeper understanding of the target market and audience.

Project documentation

Developed detailed documentation outlining project scope, objectives, and technical and functional requirements, serving as a reference guide throughout the project.

User-centric design

Designed user interfaces and experiences that aligned with user needs, preferences, and industry best practices, promoting user satisfaction and engagement.

Technical planning

Collaborated with subject-matter experts, including the CTO, to devise a scalable, high-performing technical solution addressing identified requirements and constraints.

Infrastructure setup

The inception of sound algorithm design and implementation began with proper system configuration. Modsen’s AI/ML experts laid the groundwork, employing Python for programming, TensorFlow and PyTorch for creating a dataset and training machine learning models, PostgreSQL for databases, and Git, Docker, along with pertinent testing and debugging tools for seamless development and deployment with zero downtime.

Architecture design 

Our team prioritized scalability, performance, and security while architecting the custom audio processing application, tailored for its SaaS environment. We integrated front-end, back-end, database, AI/ML modules, and APIs to ensure efficient handling of user traffic and data loads. Recognizing the diverse needs of businesses, our specialists implemented robust multi-tenancy support for seamless service delivery. Modsen’s configuration management system allowed for feature, workflow, and interface customization to suit each client’s unique requirements, with developer intervention available as necessary.

Agile implementation 

Acoustic signal processing experts at Modsen implemented the code in agile sprints, typically two weeks long, with daily stand-ups, sprint planning and reviews, and retrospectives. This process facilitated iterative development, continuous improvement, and adaptability to changing requirements and feedback.

Quality assurance and testing

The emotion recognition solution was rigorously tested by our QA specialists step by step to make sure it met quality standards and functional requirements. Load testing and security testing were also performed to validate the system’s reliability and safety.

Stakeholder demonstration

Throughout the development process, tangible achievements were demonstrated to clients after each sprint, allowing them to track progress, make suggestions, and provide feedback, thus ensuring that the final product met all stakeholders’ expectations.

Deployment and integration 

Prior to moving the custom sound processing software to production, it underwent rigorous testing and evaluation on a staging server. Following deployment, stakeholders, including clients and end users, were invited to provide feedback for future enhancements and improvements.

Third-party audits and certification 

We conducted third-party audits to guarantee compliance with industry standards and regulations, such as data protection and security requirements. Relevant certifications or approvals were obtained as required by the project’s industry, geography, and target market.

Client acceptance testing

Finally, Modsen conducted thorough user acceptance testing sessions with the client to confirm that the developed audio data analysis and visualization services met their expectations and complied with agreed-upon requirements. The process involved hands-on testing of key functionalities and features, with feedback integrated for final adjustments before deployment to production.