Gauging crowd sentiment has traditionally been a challenge. Our client wished to develop a system for analyzing audio from public gatherings and understanding the crowd vibe. To address this, we at BayRock Labs helped the client develop a data-driven solution using machine learning. By analyzing audio from public gatherings, the system deciphered crowd vibe through factors like gender and music, empowering organizers to create a more immersive experience.
Pilot Testing with Nightlife Venues
The models were successfully tested with audio data collected from real-world nightlife venues.
Improved Event Organization
By tailoring the experience to the identified audience demographics and preferences (e.g., music selection, lighting), event organizers witnessed a significant decrease in audience dissatisfaction.
Enhanced Audience Engagement
Utilizing insights from the music identification model, organizers could select music that resonated with the crowd, leading to a more engaged and interactive audience.
By analyzing both the gender distribution (e.g., identifying a crowd skewed towards a specific gender) and the background music (e.g., recognizing high-energy dance music), the system provided valuable insights into the overall demographics and mood of the audience.
Trained on a massive dataset (> 1 million) of labeled audio recordings with voices of various genders. Employs a Random Forest Classifier to identify speaker gender distribution within the crowd audio with high accuracy.
Leverages AcoustID, an open-source audio identification service within the Azure cloud platform. Trained on a comprehensive music library to recognize different musical genres and styles.
BayRock Labs has been a stalwart in the tech industry for over 12+ years, recognized as Silicon Valleyâs leading product engineering entity. Our expertise lies in crafting cutting-edge software solutions that make a global impact. Our dedication to innovation consistently earns us the reputation as the most reliable and trusted partner in the industry.
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