
追风Lab .eth🌿|Nov 13, 2025 12:19
@ silencioNetwork, which has been issued, is a decentralized audio data network focused on building a "global ear for AI and robots". It collects and processes real-world audio data, including noise mapping, voice training, and environmental sound intelligence, by building a privacy first distributed network. The project is based on the DePIN architecture and built on the Peaq chain, attracting over 1.2 million users distributed in more than 180 countries. The core vision of Silencio is to address the issue of "deafness" in audio processing for AI models - it is estimated that 70% of human contexts involve audio, but most AI models lack high-quality and diverse audio training data.
At present, the market value is about 3.63 million, and the circulating supply of FDV 13.8M is about 2.592 billion SLC. The market value of the project is not high, it can be achieved through
Participate in the activity and receive SLC airdrops
Can participate in the path:
1. Download the Silencio App( https://qr.silencio.network/app )Register and activate the microphone, and the app will automatically measure environmental noise. Record 10-30 minutes daily and rank on the leaderboard.
2. Registration: https://ai.silencio.store/campaigns/english-uk?ref=BDDBFB Participate in the "Campaign Mode" recording and invite friends for additional rewards.
3. Do the Zealy task: https://(Zealy. io)/kw/silencionenetwork/questboard
Silencio's product system is divided into three main levels: noise mapping, voice AI training, and environmental intelligence.
·Silencio App: This is a mobile application that contributes data by measuring environmental noise levels and earning SLC. The application emphasizes privacy protection and anonymous processing of user data.
·Voice AI Training Platform: Silencio's flagship service, where users record voice samples to train AI models, supporting multiple languages and scenarios.
·Environmental Intelligence and Robotics Applications: Silencio provides SoundCheck tool for global scale noise mapping and extends to the fields of robotics and smart cities. The data is used to train voice assistants and capture the diversity of sounds in the real world.
Silencio's native token SLC is the core of the ecosystem, used to reward contributors and governance. Unlike other projects, it has strong practical utility and scarcity mechanisms to ensure its long-term value:
1. Real application scenarios: SLC has driven global noise pollution monitoring, smart city data planning, environmental compliance reporting, and research datasets, serving real customers such as Google Maps and Airbnb. Expanding to AI voice training in the future, providing "ear" data for robots.
2. Regulatory compliance and institutional level trust: BlockSound Foundation has obtained EU MiCA compliance certification, making SLC the first compliant decentralized data token that supports transactions and institutional participation across the EU.
3. Token economy model: The total supply is limited, and the circulation is reduced through a burning mechanism (Alpha Burn has been initiated, destroying 2.3 billion+unclaimed airdrop tokens); The pledge mechanism encourages holding, and 257M+SLC has been pledged. 541M SLC is distributed to active contributors through blockchain lottery every month.
4. Liquidity and Ecological Integration: Listed on KuCoin, AerodromeFi (Base Chain), and MechaniDEX (Peaq Chain), supporting on chain trading and liquidity mining.
Silencio Network is strategically positioned at the intersection of DePIN and AI, filling the gap in AI training through user driven audio data collection. Its privacy priority, compliance orientation, and reward mechanism make it stand out in the competition. In the future, with the expansion of robotics and smart city applications, Silencio is expected to achieve exponential growth. Long term optimism about the DePIN track and the growth of AI data demand, but it is recommended to wait for confirmation of technical oversold rebound or stabilization of key support levels before considering configuration.