Like Dirt-Samples, but cleaned up

Overview

Clean-Samples

Like Dirt-Samples, but cleaned up, with clear provenance and license info (generally a permissive creative commons licence but check the metadata for specifics).

The bin/meta.py python script is a reference implementation that can make a '.cleanmeta' metadata file for your own sample pack folder. See below for how to use it and contribute a sample pack of your own.

If you want to use these outside the Tidal/SuperDirt/SuperCollider ecosystem you are very welcome. You're encouraged to join discussion in the github issue tracker so that we can develop a standard way to share and index/signpost these packs.

See /tidalcycles/sounds-repetition for an example sample pack which has two sets of samples in it.

How to contribute a sample pack

Please only contribute samples if you are happy to share them under a permissive license such as CC0 or a similar creative commons license.

If you are unfamiliar with the 'git' software, please create an issue here, with a short description of your samples and a link to them and someone should be along to help shortly.

If you are familiar with git and running python scripts (or happy to learn), please follow the below instructions. This is all new - if anything is unclear please create an issue, thanks!

  1. Get your samples together in .wav format, editing them if necessary (see below for advice).

  2. Create a new repository. This isn't essential, but consider putting 'sounds-' in front of its name, e.g. 'sounds-303bass' for your 303 bass samples.

  3. Add your samples to the repository. For an example of how to organise them, see this sample pack: tidalcycles/sounds-repetition, which has two sets of samples, with a subfolder for each.

  4. Create a '.cleanmeta' metadata file for each subfolder. Again, see tidalcycles/sounds-repetition for examples. There is a python script bin/meta.py which can generate the metadata file for you, run it without parameters for help. Here is an example commandline, that was used to generate repetition.cleanmeta:

    ../Clean-Samples/bin/meta.py --maintainer alex --email [email protected] --copyright "(c) 2021 Alex McLean" --license CC0 --provenance "Various dodgy speech synths" --shortname repetition --sample-subfolder repetition/ --write .
    

    After generating the file, edit it with a text editor to fill in any missing info.

  5. When ready, add te URL of your repository to the https://github.com/tidalcycles/Clean-Samples/blob/main/Clean-Samples.quark for the Clean-Samples quark) in a pull request. You could also add it to the SuperCollider quarks database, or we can do that for you if you prefer, so that we can accept the PR to Clean-Samples once it's accepted as a quark.

Advice for preparing samples

You can use free/open source software like audacity for editing samples.

As a minimum, be sure to trim any silence from beginning/end of the samples, and that the start and end of the sample is at zero to avoid clicks (you might need to fade in / fade out by a tiny amount to achieve this).

Consider adjusting the volume/loudness too, for example normalising to -1.0db - but this is very subjective and will depend on the nature of the samples and the music they're used with. For example distorted gabba samples are intended to be very loud, and a whisper is intended to sound silent. The average non-percussive sample should be around -23dB RMS. Samples shouldn't exceed 0dB true peak. EBU recommends -1dBTP at 4x-oversampling. Samples generally shouldn't have DC offset, although e.g. some kick drum samples naturally have non-zero mean.

For more advice, you could join the discussion here.

Thanks!

Owner
TidalCycles
Live coding environment for making patterns
TidalCycles
Turning pixels into virtual points for multimodal 3D object detection.

Multimodal Virtual Point 3D Detection Turning pixels into virtual points for multimodal 3D object detection. Multimodal Virtual Point 3D Detection, Ti

Tianwei Yin 204 Jan 08, 2023
Official Pytorch implementation for Deep Contextual Video Compression, NeurIPS 2021

Introduction Official Pytorch implementation for Deep Contextual Video Compression, NeurIPS 2021 Prerequisites Python 3.8 and conda, get Conda CUDA 11

51 Dec 03, 2022
[ICRA2021] Reconstructing Interactive 3D Scene by Panoptic Mapping and CAD Model Alignment

Interactive Scene Reconstruction Project Page | Paper This repository contains the implementation of our ICRA2021 paper Reconstructing Interactive 3D

97 Dec 28, 2022
Face Alignment using python

Face Alignment Face Alignment using python Input Image Aligned Face Aligned Face Aligned Face Input Image Aligned Face Input Image Aligned Face Instal

Sajjad Aemmi 28 Nov 23, 2022
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Federico Galatolo 172 Dec 22, 2022
A Python package for time series augmentation

tsaug tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to conn

Arundo Analytics 278 Jan 01, 2023
Official code for the paper "Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks".

Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks This repository contains the official code for the

Linus Ericsson 11 Dec 16, 2022
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

DeepMLS: Deep Implicit Moving Least-Squares Functions for 3D Reconstruction This repository contains the implementation of the paper: Deep Implicit Mo

103 Dec 22, 2022
Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception-like Unit (PyTorch) (CVPR-2021) DBB is a powerful ConvNet building block to replace regul

253 Dec 24, 2022
Official implementation for "Image Quality Assessment using Contrastive Learning"

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

Pavan Chennagiri 67 Dec 30, 2022
🥇Samsung AI Challenge 2021 1등 솔루션입니다🥇

MoT - Molecular Transformer Large-scale Pretraining for Molecular Property Prediction Samsung AI Challenge for Scientific Discovery This repository is

Jungwoo Park 44 Dec 03, 2022
Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Phil Wang 383 Jan 02, 2023
A collection of papers about Transformer in the field of medical image analysis.

A collection of papers about Transformer in the field of medical image analysis.

Junyu Chen 377 Jan 05, 2023
Code for database and frontend of webpage for Neural Fields in Visual Computing and Beyond.

Neural Fields in Visual Computing—Complementary Webpage This is based on the amazing MiniConf project from Hendrik Strobelt and Sasha Rush—thank you!

Brown University Visual Computing Group 29 Nov 30, 2022
Yolo algorithm for detection + centroid tracker to track vehicles

Vehicle Tracking using Centroid tracker Algorithm used : Yolo algorithm for detection + centroid tracker to track vehicles Backend : opencv and python

6 Dec 21, 2022
[TNNLS 2021] The official code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement"

CSDNet-CSDGAN this is the code for the paper "Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement" Environment Preparing pyt

Jiaao Zhang 17 Nov 05, 2022
Python package to generate image embeddings with CLIP without PyTorch/TensorFlow

imgbeddings A Python package to generate embedding vectors from images, using OpenAI's robust CLIP model via Hugging Face transformers. These image em

Max Woolf 81 Jan 04, 2023
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
Merlion: A Machine Learning Framework for Time Series Intelligence

Merlion: A Machine Learning Library for Time Series Table of Contents Introduction Installation Documentation Getting Started Anomaly Detection Foreca

Salesforce 2.8k Dec 30, 2022
Code for the paper Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

IMAGINE: Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration This repo contains the code base of the paper Language as a Cog

Flowers Team 26 Dec 22, 2022