{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9852736","patent":{"patent_number":"US-9852736","title":"Multi-mode audio recognition and auxiliary data encoding and decoding","assignee":null,"inventors":[],"filing_date":"2016-04-04T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G10L","G10L"],"num_claims":19,"abstract":"Audio signal processing enhances audio watermark embedding and detecting processes. Audio signal processes include audio classification and adapting watermark embedding and detecting based on classification. Advances in audio watermark design include adaptive watermark signal structure data protocols, perceptual models, and insertion methods. Perceptual and robustness evaluation is integrated into audio watermark embedding to optimize audio quality relative the original signal, and to optimize robustness or data capacity. These methods are applied to audio segments in audio embedder and detector configurations to support real time operation. Feature extraction and matching are also used to adapt audio watermark embedding and detecting."},"analysis":{"summary":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent introduces a sophisticated system for enhancing audio watermark embedding and detection processes. At its core, this innovation addresses the long-standing challenge of integrating auxiliary data into audio signals without compromising sound quality or data robustness.\n\nThe problem it solves is the inherent trade-off in traditional audio watermarking, where methods often sacrifice either the imperceptibility of the watermark or its resilience against various audio manipulations. Conventional systems struggle to adapt to diverse audio content, leading to suboptimal performance across different genres or listening environments.\n\nThis patent's key technical approach involves multi-mode adaptive audio signal processing. It begins with an audio classification stage, where the system intelligently categorizes the incoming audio. Based on this classification, it dynamically adjusts the parameters for watermark embedding and detection. This includes adapting the watermark signal structure, utilizing advanced perceptual models to ensure inaudibility, and optimizing insertion methods for maximum robustness or data capacity. A crucial aspect is the integrated, real-time perceptual and robustness evaluation, which continuously fine-tunes the embedding process to balance audio quality with data integrity. Furthermore, it employs feature extraction and matching to enhance the precision and effectiveness of the watermarking.\n\nThe business value and applications of this technology are extensive. It enables superior content authentication, broadcast monitoring, and digital rights management by providing a highly robust and imperceptible means of embedding metadata. Industries like media, entertainment, advertising, and even automotive (for in-car audio systems) can leverage this innovation for secure content delivery, personalized audio experiences, and enhanced interactive applications. The real-time operational capability makes it suitable for live streaming and dynamic content environments.\n\nThe market opportunity lies in creating a new standard for intelligent audio content. By offering a solution that overcomes previous limitations, this patent opens doors for more sophisticated audio-driven services, improved content monetization, and enhanced user experiences. It positions its adopters at the forefront of audio technology, offering a distinct competitive advantage in a rapidly evolving digital landscape.","layman_explanation":"### What Problem Does This Solve?\nImagine you're a major media company, a streaming service, or even an advertiser, and you create valuable audio content—music, podcasts, commercials, or even movie soundtracks. You need a way to track this content, prove ownership, or embed special instructions (like 'play this ad next'). The problem is, when you try to embed this 'auxiliary data' (like a digital watermark) into the audio, you often face a dilemma: either the data is easily stripped out or destroyed (especially when the audio is compressed, edited, or re-recorded), or the embedding process makes the audio sound noticeably worse. Existing solutions often force a trade-off, making them impractical for high-quality, high-value content where both fidelity and security are paramount. This patent addresses that fundamental challenge, aiming to deliver both without compromise.\n\n### How Does It Work?\nThe **Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding** patent isn't about a single, rigid way of hiding data. Instead, it introduces a 'smart' system that adapts its approach based on the audio itself. Think of it like a highly skilled chameleon: it first 'listens' to the audio and figures out its characteristics. Is it a quiet, delicate classical piece? A loud, busy rock track? A segment of clear speech? This is the 'audio recognition' part.\n\nOnce it 'knows' the audio type, it then selects the *best* strategy for 'auxiliary data encoding and decoding.' This means it dynamically chooses the most effective way to embed the hidden information, ensuring it's virtually imperceptible to the human ear while being incredibly resilient against common audio manipulations. It uses advanced 'perceptual models' (how humans hear sound) to find the 'quietest' spots in the audio spectrum to hide data. Simultaneously, it constantly evaluates the data's 'robustness'—its ability to survive—and adjusts its method in real-time. It's like having a master craftsman who picks the perfect tool and technique for each unique piece of wood, ensuring the hidden joint is strong and invisible.\n\n### Why Does This Matter?\nThis innovation matters because it unlocks new possibilities for businesses across numerous sectors. For media and entertainment, it means more secure content distribution and more accurate tracking of intellectual property, leading to better monetization and reduced piracy. For advertisers, it allows for dynamic, context-aware ad insertion and precise campaign measurement. In the automotive industry, it could enable advanced in-car infotainment systems that deliver personalized or location-aware audio experiences. Streaming services can enhance their digital rights management and provide richer, interactive content. The ability to embed robust, imperceptible data allows for a new layer of intelligence within audio, transforming it from a passive medium into an active carrier of information. This translates into increased operational efficiency, stronger content protection, and opportunities for creating entirely new products and services.\n\n### What's Next?\nThis patent lays the groundwork for a future where audio is not just heard but also 'understood' and 'interacted with' on a deeper level. We can expect to see wider adoption in digital content supply chains, driving new standards for metadata embedding and content authentication. It paves the way for advanced applications in augmented reality, smart environments, and highly personalized digital experiences where audio plays a central, intelligent role. For investors, this represents a foundational technology that will fuel innovation and market growth in the rapidly expanding digital audio ecosystem, promising significant returns on investment through licensing and integrated solutions.","technical_analysis":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent (US-9852736) details a highly advanced system for embedding and detecting auxiliary data within audio signals. This invention distinguishes itself through its adaptive, multi-modal approach to audio watermarking, directly addressing the limitations of static methodologies by integrating intelligent audio classification and real-time optimization.\n\n**Technical Architecture:**\nThe system's architecture is built around a dynamic processing pipeline that begins with an input audio stream. This stream is fed into an **Audio Classification Module**, which performs real-time analysis to categorize the audio content (e.g., speech, music, ambient noise, specific genres). This classification is not merely descriptive but prescriptive, guiding the subsequent watermarking stages.\n\nFollowing classification, the audio proceeds to an **Adaptive Watermark Embedder**. This module is revolutionary in its ability to dynamically select and configure watermark signal structures, perceptual models, and insertion methods. Instead of a fixed algorithm, it draws upon a library of techniques, applying the most suitable one based on the audio characteristics identified by the classification module. For instance, in a perceptually critical segment, it might employ a spread spectrum technique with minimal energy, guided by sophisticated psychoacoustic models. In a more robust segment, it could opt for a higher data capacity embedding.\n\nA critical component is the **Perceptual and Robustness Evaluation Module**, which operates in a closed-loop feedback mechanism with the embedder. This module continuously assesses two key metrics: the perceptual quality (audibility of the watermark) and its robustness (resistance to attacks like compression, filtering, or noise). This real-time feedback allows the embedder to fine-tune its parameters on the fly, ensuring optimal balance between imperceptibility and resilience – a core challenge in traditional watermarking. Perceptual models, possibly based on human auditory system characteristics like critical bands and masking thresholds, are central to this evaluation.\n\nFinally, **Feature Extraction and Matching** techniques are integrated to further enhance the system's performance. Feature extraction identifies unique characteristics of the audio signal, which can be used for synchronized detection or to identify optimal 'hiding places' for the watermark. The matching component then correlates these features to improve the accuracy and reliability of watermark detection, even under challenging conditions.\n\n**Implementation Details and Algorithm Specifics:**\nThe implementation likely involves a combination of advanced signal processing algorithms and machine learning techniques:\n\n*   **Audio Classification**: This could leverage deep learning models such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) trained on vast datasets for robust content categorization. Feature vectors for classification might include Mel-frequency cepstral coefficients (MFCCs), spectral centroids, or zero-crossing rates.\n*   **Adaptive Watermark Design**: The selection of watermark signal structure could involve Direct Sequence Spread Spectrum (DSSS) for robustness, Orthogonal Frequency-Division Multiplexing (OFDM) for multi-carrier embedding, or phase modulation techniques. Data protocols would include error correction codes (e.g., Reed-Solomon, convolutional codes) that are adaptively chosen based on the desired robustness level and data capacity.\n*   **Perceptual Models**: These would be based on established psychoacoustic principles, such as those used in MP3 or AAC encoding, to determine frequency-dependent masking thresholds. The embedder would aim to place watermark energy below these thresholds.\n*   **Robustness Evaluation**: This involves simulating common audio distortions (e.g., re-quantization, low-pass filtering, time-scaling) and measuring the watermark's survival rate, providing feedback for adaptive adjustments.\n*   **Feature Extraction**: Techniques like audio fingerprinting (e.g., using robust hash functions on spectral features) or transient detection algorithms would be employed to create unique identifiers for audio segments, aiding in synchronization and detection.\n\n**Integration Patterns and Performance Characteristics:**\nThe system is designed for real-time operation, suggesting a highly optimized software and potentially hardware-accelerated (e.g., DSPs, FPGAs) implementation. Its modular nature allows for flexible integration into existing audio processing pipelines, such as those used in broadcast studios, streaming platforms, or content management systems. The adaptive nature of this technology implies superior performance compared to fixed-parameter systems, particularly in scenarios with diverse audio content or varying transmission channels. It promises higher data capacity for auxiliary information without sacrificing audio quality, alongside enhanced robustness against a wider range of signal manipulations.","business_analysis":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent (US-9852736) represents a significant leap in audio technology, unlocking substantial market opportunities and competitive advantages across various industries. This invention's ability to adaptively embed and detect auxiliary data in audio signals, while maintaining uncompromised quality, positions it as a critical enabler for the next generation of digital media and content management.\n\n**Market Opportunity Size:**\nThe global audio content market is colossal, encompassing music, podcasts, audiobooks, broadcasting, streaming, and gaming, valued in the hundreds of billions of dollars and growing. Within this, the market for content protection, digital rights management (DRM), and interactive audio technologies is rapidly expanding. This invention targets a crucial pain point in this ecosystem, offering a superior solution for content authentication, usage tracking, and personalized experiences. The addressable market for this technology includes media and entertainment companies, advertising agencies, broadcast networks, streaming services, automotive infotainment providers, and smart home device manufacturers. Its potential extends to any sector requiring robust, imperceptible data embedding in audio, suggesting a multi-billion dollar market for licensing, integration, and service provision.\n\n**Competitive Advantages:**\nThis technology offers several distinct competitive advantages:\n\n1.  **Superior Performance**: By dynamically adapting watermarking strategies based on audio classification, the system overcomes the traditional trade-off between audio quality and watermark robustness. This 'best of both worlds' approach is a major differentiator.\n2.  **Real-time Operation**: The ability to perform adaptive embedding and detection in real-time makes it suitable for live broadcasts, interactive applications, and dynamic content streams, where traditional batch-processing methods fall short.\n3.  **Enhanced Data Capacity & Resilience**: Adaptive watermark signal structures and perceptual models allow for higher data payloads and greater resilience against a wider range of signal distortions, offering more reliable data transmission.\n4.  **Versatility**: Its multi-mode nature makes it adaptable to diverse audio content types and application requirements, from subtle metadata embedding in high-fidelity music to robust tracking in noisy broadcast environments.\n\n**Revenue Potential and Business Models:**\nRevenue streams could be generated through:\n\n*   **Licensing**: Licensing the patented technology to media companies, streaming platforms, and hardware manufacturers.\n*   **Software-as-a-Service (SaaS)**: Offering cloud-based audio watermarking and content tracking services.\n*   **Integration Services**: Providing bespoke integration of the technology into existing enterprise content management and broadcast systems.\n*   **Premium Analytics**: Selling data insights derived from robust content tracking and usage patterns.\n*   **Hardware Solutions**: Developing specialized hardware (e.g., DSPs) that incorporate the patented algorithms for high-performance applications.\n\n**Strategic Positioning:**\nThis innovation allows companies to strategically position themselves as leaders in intelligent audio technology. For content owners, it provides an unparalleled level of control and insight into their intellectual property. For platform providers, it enables richer, more secure, and personalized user experiences. It can also serve as a foundational technology for new product categories, such as augmented audio reality or adaptive in-car sound systems that deliver context-aware information.\n\n**ROI Projections:**\nInvesting in or adopting this technology promises significant ROI through:\n\n*   **Reduced Piracy & Improved Monetization**: More robust content tracking can lead to better enforcement of digital rights and increased revenue from legitimate usage.\n*   **Operational Efficiency**: Automated, real-time content monitoring reduces manual effort and improves response times for compliance and advertising.\n*   **Enhanced User Engagement**: Personalized and interactive audio experiences driven by embedded data can increase user retention and satisfaction.\n*   **New Product Development**: The technology enables the creation of innovative audio products and services, opening new revenue channels.\n\nIn essence, the Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent offers a compelling proposition for businesses looking to future-proof their audio strategies, secure their assets, and unlock new dimensions of auditory interaction.","faqs":[{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent (US-9852736) describes an advanced system designed to embed and detect hidden, or 'auxiliary,' data within audio signals. Unlike traditional methods, this invention employs an intelligent, adaptive approach.\n\nAt its core, it works by first analyzing and 'recognizing' the characteristics of an audio segment – for example, whether it's speech, music, or ambient noise. Based on this classification, the system then dynamically adjusts how it embeds and later extracts the auxiliary data.\n\nThe goal is to achieve a perfect balance: ensuring the hidden data is incredibly robust and resistant to being removed or corrupted, while simultaneously guaranteeing that the audio quality remains pristine and the embedded data is completely inaudible to the human ear. This adaptive strategy is a significant leap forward in audio signal processing.","question":"What is Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding?"},{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding system operates through a sophisticated, multi-stage process. First, an 'audio classification' module analyzes the incoming audio to identify its specific characteristics and content type. This could involve using machine learning algorithms to discern between speech, various music genres, or environmental sounds.\n\nNext, based on this real-time classification, an 'adaptive watermark embedder' dynamically selects the most appropriate method for inserting the auxiliary data. This involves choosing from different watermark signal structures, perceptual models (which exploit how humans hear sound to hide data), and insertion techniques.\n\nCrucially, the system includes a continuous, real-time feedback loop. It constantly evaluates both the audio quality (ensuring the watermark is inaudible) and the data's robustness (its resistance to distortion). This allows for on-the-fly adjustments to optimize performance. Additionally, 'feature extraction and matching' techniques are used to precisely place the watermark and ensure reliable detection, even if the audio is altered.","question":"How does Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding work?"},{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent primarily solves the long-standing dilemma in audio watermarking: the trade-off between watermark imperceptibility and robustness. Historically, embedding auxiliary data into audio either resulted in audible degradation of the sound quality or the watermark being easily removed or destroyed by common audio processing (like compression or re-recording).\n\nTraditional methods often use a static approach, applying the same embedding strategy regardless of the audio content. This leads to suboptimal performance across diverse audio types or listening environments. This invention overcomes this by providing an adaptive, intelligent solution that can maintain both high audio fidelity and strong data integrity simultaneously, a critical need for modern digital media and content management.\n\nThis solves crucial problems for content owners, broadcasters, and advertisers by enabling reliable content tracking, copyright protection, and data delivery without compromising the user experience.","question":"What problem does Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding solve?"},{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent (US-9852736) was filed on April 4, 2016, and granted on December 26, 2017. While the specific inventors are not detailed in the provided abstract, patents are typically the result of extensive research and development by teams of engineers and scientists.\n\nSuch complex innovations in audio signal processing usually involve experts in digital signal processing, psychoacoustics, machine learning, and computer science. These individuals contribute their expertise to develop the sophisticated algorithms and adaptive systems described in this patent.\n\nThe assignee, which is the entity to whom the patent rights are transferred (often a company), is also not provided in this specific abstract, but they would be the organization that funded and directed the research leading to this significant invention.","question":"Who invented Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding?"},{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent offers several significant benefits that set it apart from prior art in audio watermarking.\n\nFirstly, it delivers an optimal balance between audio quality and data robustness. By adaptively adjusting embedding methods based on audio characteristics, it ensures the embedded data is inaudible while being highly resilient against various distortions, a feat rarely achieved simultaneously by static systems. Secondly, its real-time operational capability makes it suitable for dynamic applications like live broadcasting and interactive streaming, where immediate processing and adaptation are crucial.\n\nThirdly, this technology enhances data capacity, allowing for more auxiliary information to be embedded reliably. Finally, its versatility across different audio types and environments makes it a robust, future-proof solution for content owners, media platforms, and technology developers seeking advanced audio intelligence.","question":"What are the key benefits of Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding?"},{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent significantly differs from prior art in its adaptive, context-aware approach to audio watermarking. Traditional methods typically employ a fixed set of parameters for embedding auxiliary data, regardless of the specific audio content.\n\nThis 'one-size-fits-all' strategy often leads to compromises: a watermark might be robust but audible, or imperceptible but easily removed. This invention, however, first performs 'audio classification' to understand the nature of the audio segment. It then dynamically selects the most appropriate embedding technique, perceptual model, and data protocol from a multi-mode repertoire.\n\nFurthermore, it integrates real-time perceptual and robustness evaluation in a feedback loop, continuously optimizing the balance between audio quality and data integrity. This level of intelligent adaptation and dynamic optimization is a key differentiator, allowing it to outperform static prior art in both imperceptibility and resilience.","question":"How is Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding different from prior art?"},{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent holds transformative potential across a wide array of industries that rely heavily on audio content. The media and entertainment sector, including streaming services, broadcasters, music labels, and film studios, will benefit immensely from enhanced digital rights management, content authentication, and anti-piracy measures.\n\nAdvertising and marketing industries can leverage this technology for highly accurate ad attribution, campaign measurement, and dynamic, context-aware ad insertion. The gaming industry can create more immersive and interactive audio experiences by embedding hidden data that triggers in-game events or provides contextual information.\n\nBeyond these, the automotive sector (for advanced infotainment systems), smart home device manufacturers, and even accessibility technology providers can utilize this innovation to deliver more intelligent, secure, and personalized audio experiences. It essentially provides a foundational technology for any application requiring robust, imperceptible data embedding in audio streams.","question":"What industries will Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding impact?"},{"answer":"The patent for Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding, identified as US-9852736, was filed on April 4, 2016. This marks the date when the patent application was submitted to the patent office, initiating the examination process.\n\nFollowing the examination and approval, the patent was subsequently granted and published on December 26, 2017. The publication date signifies when the patent officially became public record and its rights were formally assigned.\n\nThese dates are crucial for understanding the timeline of the invention's development and its entry into the intellectual property landscape. The relatively quick turnaround from filing to grant reflects the novelty and significance of the technology in the field of audio signal processing.","question":"When was Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding filed/granted?"},{"answer":"The commercial applications of the Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent are extensive and diverse, spanning multiple high-value sectors. One primary application is in **Digital Rights Management (DRM)** and **Content Authentication**, enabling media companies to embed robust, invisible watermarks for tracking content usage, preventing piracy, and ensuring proper royalty distribution across global platforms.\n\nAnother key area is **Broadcast Monitoring and Analytics**, where the technology can precisely identify and track audio content (including advertisements) in real-time across TV, radio, and streaming, providing invaluable data for advertisers and compliance. In **Interactive Audio Experiences**, it can facilitate personalized content delivery, augmented reality audio, or trigger in-app actions based on embedded data.\n\nFurthermore, it can be applied in **Smart Advertising** for dynamic ad insertion and granular campaign measurement, and in **Automotive Infotainment** for context-aware audio services. The ability to embed robust, imperceptible data makes it ideal for any commercial venture requiring secure, intelligent, and high-quality audio data management.","question":"What are the commercial applications of Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding?"},{"answer":"The Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent lays a strong foundation for numerous future developments in audio technology. We can expect to see further advancements in the **intelligence of audio classification**, potentially incorporating more sophisticated deep learning models that can identify nuanced audio characteristics and even emotional content.\n\nFuture iterations may also explore **new adaptive watermark signal structures** that are even more resilient to emerging audio processing techniques or adversarial attacks, pushing the boundaries of data capacity and imperceptibility. Integration with **blockchain technology** could provide immutable records of content ownership and usage, leveraging the robust watermarking for enhanced trust and transparency.\n\nBeyond core watermarking, this technology could evolve into a universal standard for **intelligent audio metadata embedding**, allowing audio content to carry rich, dynamic information that adapts to its playback environment or user preferences. This would pave the way for highly personalized, context-aware, and interactive audio experiences in smart environments, augmented reality, and next-generation communication systems, fundamentally changing how we perceive and interact with sound.","question":"What are the future developments expected for Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding?"}],"topics":["multi-mode audio recognition","auxiliary data encoding","audio decoding","audio watermarking","adaptive signal processing","landscape","digital","audio"],"tech_cluster":null},"seo":{"title":"Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding - Patent US-9852736","description":"Discover the Multi-mode Audio Recognition and Auxiliary Data Encoding and Decoding patent. This innovation enhances audio watermarking by adapting embedding and detection based on audio classification, ensuring optimal quality and robustness for auxiliary data. Explore adaptive signal processing and real-time operation.","keywords":["multi-mode audio recognition","auxiliary data encoding","audio decoding","audio watermarking","adaptive signal processing","audio classification","perceptual models","robustness evaluation","real-time audio","feature extraction","G10L patent","US-9852736","patentable app"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9852736","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9852736","citation_suggestion":"Patentable. \"Multi-mode audio recognition and auxiliary data encoding and decoding\" (US-9852736). https://patentable.app/patents/US-9852736","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9852736","json":"https://patentable.app/api/llm-context/US-9852736","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T10:56:11.777Z"}