semantiva_audio 0.0.1

Audio specialization of Semantiva framework.

Semantiva - Audio specialization

Overview

semantiva_audio is the audio specialization of the Semantiva framework. It offers tailored data types and operations for audio operations. This module provides algorithms for processing single-channel and dual-channel audio data. It includes specialized algorithms for both types, as well as utilities for merging dual-channel audio into a single-channel format and expanding single-channel audio into dual-channel.

Installation

This module is available via Pypi:

pip install semantiva_audio

This command should install the package and all its dependencies, including Semantiva itself.

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Getting Started: A Minimal Example

Below is a quick demonstration showing how to use the specialization. This assumes that the installation section was completed successfully. In this example, we will create a custom audio algorithm SingleChannelAudioMultiplyAlgorithm, which multiplies single-channel audio data by a given factor.

import numpy as np
from semantiva_audio.audio_data_types import SingleChannelAudioDataType
from semantiva_audio.audio_operations import SingleChannelAudioAlgorithm

class SingleChannelAudioMultiplyAlgorithm(SingleChannelAudioAlgorithm):
    """
    A specialized algorithm to multiply single-channel audio data by a given factor.
    """

    def _operation(self, data, factor):
        self.logger.debug("Inside the SingleChannelAudioMultiplyAlgorithm")
        multiplied_data = data.data * factor
        return SingleChannelAudioDataType(multiplied_data)

# Example usage of SingleChannelAudioMultiplyAlgorithm

# Create an instance of the multiplication algorithm
multiply_algorithm = SingleChannelAudioMultiplyAlgorithm()

# Example input: Single-channel audio data
input_audio = SingleChannelAudioDataType(np.random.rand(1000))

# Define a multiplication factor
factor = 2.0

# Process the data using the algorithm
output_audio = multiply_algorithm(input_audio, factor)

Explanation:

1. Class Inheritance:

   • SingleChannelAudioMultiplyAlgorithm extends SingleChannelAudioAlgorithm, which defines input/output data types.

2. Custom Operation:

   • The _operation(data, factor) method multiplies every element in the input audio data by a specified factor.
   • The method takes SingleChannelAudioDataType as input and returns a new instance of the same type.

3. Usage Example:

   • We create a SingleChannelAudioMultiplyAlgorithm instance.
   • Provide an example single-channel audio input with numerical values.
   • Define a multiplication factor (2.0 in this case).
   • Call _operation(input_audio, factor) to process the data.
   • The modified output is printed, showing the multiplied values.

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License

Semantiva is released under the MIT License, promoting collaborative development and broad adoption.

Acknowledgments

This framework draws inspiration from the rigorous demands of transparency and traceability in data-driven systems, particularly exemplified by the ALICE O2 project at CERN. The lessons learned from managing large-scale, high-throughput data in that environment—combined with the need for robust, domain-aligned workflows—shaped Semantiva’s emphasis on type-safe design, semantic clarity, and modular extensibility. By blending these concepts with principles of ontology-driven computing, Semantiva aims to deliver the same level of reliability and interpretability for any domain requiring advanced data processing and HPC integration.