quality Baby Sound Module & Push Button Sound Module factory

.gtr-container { font-family: 'Arial', sans-serif; color: #333; line-height: 1.6; max-width: 800px; margin: 0 auto; padding: 20px; } .gtr-heading { font-size: 18px !important; font-weight: 600; color: #2c3e50; margin-bottom: 15px; padding-bottom: 5px; border-bottom: 2px solid #e0e0e0; } .gtr-list { margin: 15px 0; padding-left: 20px; } .gtr-list li { margin-bottom: 12px; font-size: 14px !important; } .gtr-feature-title { font-weight: 600; color: #2980b9; margin-bottom: 5px; font-size: 14px !important; } .gtr-paragraph { margin-bottom: 15px; font-size: 14px !important; } The Baby Sound Module offers the following key advantages among children's educational toys: Waterproof and Durable Design: Its ABS plastic housing and waterproof PCB design withstand daily splashes and moisture, extending the toy's lifespan. Its modular construction allows for quick battery replacement (AG10/AG13 button batteries), minimizing maintenance costs. Interactive Learning: Light-activated technology enables "sound and light interaction," automatically playing music when a baby touches it or when the ambient light changes, stimulating sensory development. A built-in piano scale module allows for interactive sound books with early childhood education books. Safety and Portability: Its smooth ABS housing complies with EN71-3 toy safety standards and weighs only 15-20g, making it suitable for sewing into cloth books or plush toys. The LED flashlight features a soft light design to avoid direct sunlight into baby's eyes. Economical and Efficient Production Standardized modules support daily production of 3,000 units, reducing development costs for OEMs. Compatible with a variety of power supply options (3V button battery or rechargeable module), adapting to different price points.

  We are happy to announce that we have created a new mold for 4-10 buttons, this mold is more easy to assembly, the cost will be more cheaper, the size is 190*50*12mm,which can pass the lastest EN62115 test, Contact us to know more information

.gtr-container { font-family: 'Arial', sans-serif; font-size: 14px !important; line-height: 1.6; color: #333; max-width: 800px; margin: 0 auto; padding: 20px; background-color: #f9f9f9; border: 1px solid #e0e0e0; border-radius: 4px; } .gtr-heading { font-size: 18px !important; font-weight: 600; color: #2a5885; margin: 20px 0 10px; padding-bottom: 5px; border-bottom: 2px solid #e0e0e0; } .gtr-subheading { font-size: 16px !important; font-weight: 600; color: #3a3a3a; margin: 15px 0 8px; } .gtr-list { margin: 10px 0; padding-left: 20px; } .gtr-list li { margin-bottom: 8px; } .gtr-table { width: 100%; border-collapse: collapse; margin: 15px 0; font-size: 14px !important; } .gtr-table th { background-color: #2a5885; color: white; text-align: left; padding: 8px 12px; font-weight: 600; } .gtr-table td { padding: 8px 12px; border-bottom: 1px solid #e0e0e0; } .gtr-table tr:nth-child(even) { background-color: #f2f2f2; } .gtr-highlight { font-weight: 600; color: #2a5885; } The following is a complete technical indicator system and test method for evaluating the performance of the Baby Sound Module: I. Core Function Testing Cry Detection Accuracy Tested using a standard infant cry database (such as the Baby2023 dataset), the accuracy for classifying cry types such as hunger and pain must be ≥95%. Ambient noise suppression must meet the ISO 7779 standard test, maintaining a detection rate of 90% or higher even in 60dB background noise. Response Performance End-to-end response latency must be

.gtr-container { font-family: 'Arial', sans-serif; font-size: 14px !important; line-height: 1.6 !important; color: #333; max-width: 800px; margin: 0 auto; padding: 20px; background-color: #f9f9f9; border: 1px solid #e0e0e0; border-radius: 4px; } .gtr-heading { font-size: 16px !important; font-weight: 600; color: #2c3e50; margin: 20px 0 10px 0; padding-bottom: 5px; border-bottom: 2px solid #e0e0e0; } .gtr-subheading { font-size: 15px !important; font-weight: 600; color: #34495e; margin: 15px 0 8px 0; } .gtr-list { margin: 10px 0; padding-left: 20px; } .gtr-list li { margin-bottom: 8px; } .gtr-note { font-style: italic; color: #7f8c8d; margin-top: 15px; padding: 10px; background-color: #f0f0f0; border-left: 3px solid #95a5a6; } Typical Applications and Technical Implementations of the Baby Sound Module in Music-Playing Toys 1. Core Function Implementation Bluetooth Audio Transmission Uses a Bluetooth module supporting the A2DP protocol (such as the ANS-BT302DM) to wirelessly connect to a phone/tablet and play music stored on the host device. The module has a built-in QCC3040 chip and supports aptX low-latency encoding, ensuring audio synchronization to meet interactive needs. Local Sound Effect Playback Pre-installed 12 basic instrument sound libraries (piano, xylophone, etc.), with corresponding sound effects triggered by touch. Uses WAV format audio recording technology to support multi-track playback. 2. Hardware Integration Structural Design The module is enclosed in an environmentally friendly ABS resin casing, sealed with silicone to the toy body, and complies with EN7 standards. The speaker's vent design must take acoustic reflections into account and is typically located on the toy's belly or back. Power System Powered by a 3.7V lithium battery, the quiescent current is less than 5μA, supporting over 30 hours of continuous playback. Automatically triggers a low-battery alert (once every 2 minutes). III. Interactive Control Logic Connection Management Single-button control design: Single-click to reconnect/play, double-click to pair, long-press to clear history. Automatically shuts off after 5 minutes of no connection, supporting up to 8 devices. Sound Effect Switching Select different tones using physical buttons or touch areas, supporting melody combinations. Synchronized lighting effects enhance the immersive experience. This solution has been successfully applied to products such as musical panda dolls and electronic keyboard books, with a measured Bluetooth transmission range of up to 15 meters (in an accessible environment).

.gtr-container { font-family: 'Segoe UI', Arial, sans-serif; color: #333333; font-size: 14px !important; line-height: 1.6; max-width: 900px; margin: 0 auto; padding: 20px; } .gtr-heading { font-weight: 600; color: #2c3e50; margin: 25px 0 15px 0; font-size: 18px !important; } .gtr-subheading { font-weight: 600; color: #34495e; margin: 20px 0 10px 0; font-size: 16px !important; } .gtr-paragraph { margin-bottom: 15px; } .gtr-list { margin: 15px 0; padding-left: 25px; } .gtr-list li { margin-bottom: 10px; } .gtr-table { width: 100%; border-collapse: collapse; margin: 20px 0; font-size: 14px !important; } .gtr-table th { background-color: #f5f7fa; text-align: left; padding: 10px; border: 1px solid #ddd; font-weight: 600; } .gtr-table td { padding: 10px; border: 1px solid #ddd; } .gtr-note { font-style: italic; color: #7f8c8d; margin-top: 10px; font-size: 13px !important; } The application of deep learning models in sound recognition has formed a comprehensive technical framework. Its core value lies in achieving high-precision, multi-scenario sound feature extraction and semantic understanding through end-to-end learning. The following are key technical application directions and typical model architectures: 1. Acoustic Feature Extraction Optimization of Time-Frequency Analysis Using CNNs to automatically learn local features (such as harmonic structure and formants) from mel-spectrograms, replacing traditional manual feature engineering using MFCCs, this approach improves classification accuracy by 27% in noisy environments on the UrbanSound8K dataset. Lightweight models such as MobileNetV3, using depthwise separable convolutions and PSA attention modules, achieve 100% top-5 bird sound recognition accuracy with only 2.6M parameters. Enhanced Time Series Modeling The CRNN hybrid architecture (CNN + BiLSTM) simultaneously captures the spectral characteristics and temporal dependencies of sound events, achieving an F1 score of 92.3% for detecting sudden events such as glass breaking. Transformer uses a self-attention mechanism to process long audio sequences, achieving over 99% accuracy in classifying infant cries for hunger and pain. II. Specific Application Scenarios Application Areas Technical Solutions Performance Metrics Pet Health Monitoring RNN-Based Voice Emotion Analysis System, Supporting Classification of Over 10 Voice Types Smart Home Security End-to-End Abnormal Sound Detection Using CNN+CTC Response Latency