Bird Sounds Identifier Call ID represents a specialized tool in the wildlife identification category, offering dual recognition capabilities through visual and auditory analysis. Developed by Nhu Nguyen Thi, this iOS application addresses a specific niche for ornithology enthusiasts and casual nature observers alike. The application's core functionality centers on two primary identification methods: image recognition through device photography or uploaded images, and audio analysis through environmental recording. The visual identification system processes avian characteristics including plumage patterns, beak shape, and size proportions, while the audio analysis employs spectral analysis to isolate and match bird vocalizations against an extensive database. Both methods generate detailed species profiles containing scientific classifications, habitat information, behavioral characteristics, and high-quality reference imagery. User experience demonstrates thoughtful design with an intuitive interface featuring large capture buttons, minimal navigation layers, and clear result presentation. Real-world usage patterns indicate effectiveness in various environments, from urban parks to wilderness areas, with particular utility during dawn choruses when visual identification proves challenging. The application maintains functionality even with background ambient noise, successfully filtering out interference like traffic sounds according to user reports. User feedback trends consistently highlight satisfaction with identification accuracy and educational value. Reviewer Belqnulstein84 (June 4, 2025) reported perfect backyard bird identification through photo capture, while rpfarr (June 21, 2025) noted effective audio filtering capabilities in noisy environments. Multiple reviews mention educational applications, with Nohyn'Gutshwll76 (April 24, 2025) describing successful use as a family learning tool. The application demonstrates notable strengths in dual-mode identification and educational content depth, though limitations include dependence on lighting conditions for visual identification and potential database gaps for regional species variations. Ideal use cases encompass field research supplementation, educational activities, and enhancing recreational birdwatching experiences where immediate identification enhances engagement with natural environments.