Related papers: The Modular Audio Recognition Framework (MARF) and…
Throughout last 30 years, numerous head-related transfer function (HRTF) models have been developed and there are more to come. This paper describes a framework based on objective-oriented programming paradigm, in which each HRTF…
Music has a unique and complex structure which is challenging for both expert humans and existing AI systems to understand, and presents unique challenges relative to other forms of audio. We present LLark, an instruction-tuned multimodal…
Deep Learning models have become potential candidates for auditory neuroscience research, thanks to their recent successes on a variety of auditory tasks. Yet, these models often lack interpretability to fully understand the exact…
The availability of shared software models provides opportunities for reusing, adapting and learning from them. Public models are typically stored in a variety of locations, including model repositories, regular source code repositories,…
Flavor (Formal Language for Audio-Visual Object Representation) has been created as a language for describing coded multimedia bitstreams in a formal way so that the code for reading and writing bitstreams can be automatically generated. It…
Through the advancement in natural language processing (NLP), specifically in speech recognition, fully automated complex systems functioning on voice input have started proliferating in areas such as home automation. These systems have…
This literature focuses on doing a comparative analysis between Modular Audio Recognition Framework (MARF) and the General Intentional Programming System (GIPSY) with the help of different software metrics. At first, we understand the…
NeMo (Neural Modules) is a Python framework-agnostic toolkit for creating AI applications through re-usability, abstraction, and composition. NeMo is built around neural modules, conceptual blocks of neural networks that take typed inputs…
This paper introduces WOLF, a C++ estimation framework based on factor graphs and targeted at mobile robotics. WOLF can be used beyond SLAM to handle self-calibration, model identification, or the observation of dynamic quantities other…
We present here an exploratory and investigatory study of the requirements, design, and implementation of two opensource software systems: the Distributed Modular Audio Recognition Framework (DMARF), and the General Intensional Programming…
Federated Retrieval-Augmented Generation (Federated RAG) combines Federated Learning (FL), which enables distributed model training without exposing raw data, with Retrieval-Augmented Generation (RAG), which improves the factual accuracy of…
Automatic speech recognition (ASR) systems used on smart phones or vehicles are usually required to process speech queries from very different domains. In such situations, a vanilla ASR system usually fails to perform well on every domain.…
The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance in multi-agent path finding (MAPF) tasks has been less than…
We introduce a novel, general-purpose audio generation framework specifically designed for anomaly detection and localization. Unlike existing datasets that predominantly focus on industrial and machine-related sounds, our framework focuses…
While large language models (LLMs) are still being adopted to new domains and utilized in novel applications, we are experiencing an influx of the new generation of foundation models, namely multi-modal large language models (MLLMs). These…
Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answering, information retrieval and so on. In the context of…
The notable success of large language models (LLMs) has sparked an upsurge in building language agents to complete various complex tasks. We present AMOR, an agent framework based on open-source LLMs, which reasons with external knowledge…
Over the years, there have been campaigns to include the African languages in the growing research on machine translation (MT) in particular, and natural language processing (NLP) in general. Africa has the highest language diversity, with…
Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques. However, a major challenge is that code is written using an open, rapidly changing vocabulary due to, e.g.,…
In this article, we present Tahr, a framework that allows taking attribute grammar specifications and generating a set of software artefacts that can be used programmatically to operate on text compliant with the grammars. Tahr can be used…