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Separating Birdsong in the Wild for Classification

Birds are all around us, and just by listening, we can learn many things about our environment. Ecologists use birds to understand food systems and forest health — for example, if there are more woodpeckers in a forest, that means there’s a lot of dead wood. Because birds communicate and mark territory with songs and calls, it’s most efficient to identify them by ear. In fact, experts may identify up to 10x as many birds by ear as by sight.

In recent years, autonomous recording units (ARUs) have made it easy to capture thousands of hours of audio in forests that could be used to better understand ecosystems and identify critical habitat. However, manually reviewing the audio data is very time consuming, and experts in birdsong are rare. But an approach based on machine learning (ML) has the potential to greatly reduce the amount of expert review needed for understanding a habitat.

However, ML-based audio classification of bird species can be challenging for several reasons. For one, birds often sing over one another, especially during the “dawn chorus” when many birds are most active. Also, there aren’t clear recordings of individual birds to learn from — almost all of the available training data is recorded in noisy outdoor conditions, where other sounds from the wind, insects, and other environmental sources are often present. As a result, existing birdsong classification models struggle to identify quiet, distant and overlapping vocalizations. Additionally, some of the most common species often appear unlabeled in the background of training recordings for less common species, leading models to discount the common species. These difficult cases are very important for ecologists who want to identify endangered or invasive species using automated systems.

To address the general challenge of training ML models to automatically separate audio recordings without access to examples of isolated sounds, we recently proposed a new unsupervised method called mixture invariant training (MixIT) in our paper, “Unsupervised Sound Separation Using Mixture Invariant Training”. Moreover, in our new paper, “Improving Bird Classification with Unsupervised Sound Separation,” we use MixIT training to separate birdsong and improve species classification. We found that including the separated audio in the classification improves precision and classification quality on three independent soundscape datasets. We are also happy to announce the open-source release of the birdsong separation models on GitHub.

Birdsong Audio Separation
MixIT learns to separate single-channel recordings into multiple individual tracks, and can be trained entirely with noisy, real-world recordings. To train the separation model, we create a “mixture of mixtures” (MoM) by mixing together two real-world recordings. The separation model then learns to take the MoM apart into many channels to minimize a loss function that uses the two original real-world recordings as ground-truth references. The loss function uses these references to group the separated channels such that they can be mixed back together to recreate the two original real-world recordings. Since there’s no way to know how the different sounds in the MoM were grouped together in the original recordings, the separation model has no choice but to separate the individual sounds themselves, and thus learns to place each singing bird in a different output audio channel, also separate from wind and other background noise.

We trained a new MixIT separation model using birdsong recordings from Xeno-Canto and the Macaulay Library. We found that for separating birdsong, this new model outperformed a MixIT separation model trained on a large amount of general audio from the AudioSet dataset. We measure the quality of the separation by mixing two recordings together, applying separation, and then remixing the separated audio channels such that they reconstruct the original two recordings. We measure the signal-to-noise ratio (SNR) of the remixed audio relative to the original recordings. We found that the model trained specifically for birds achieved 6.1 decibels (dB) better SNR than the model trained on AudioSet (10.5 dB vs 4.4 dB). Subjectively, we also found many examples where the system worked incredibly well, separating very difficult to distinguish calls in real-world data.

The following videos demonstrate separation of birdsong from two different regions (Caples and the High Sierras). The videos show the mel-spectrogram of the mixed audio (a 2D image that shows the frequency content of the audio over time) and highlight the audio separated into different tracks.

High Sierras
  
Caples

Classifying Bird Species
To classify birds in real-world audio captured with ARUs, we first split the audio into five-second segments and then create a mel-spectrogram of each segment. We then train an EfficientNet classifier to identify bird species from the mel-spectrogram images, training on audio from Xeno-Canto and the Macaulay Library. We trained two separate classifiers, one for species in the Sierra Nevada mountains and one for upstate New York. Note that these classifiers are not trained on separated audio; that’s an area for future improvement.

We also introduced some new techniques to improve classifier training. Taxonomic training asks the classifier to provide labels for each level of the species taxonomy (genus, family, and order), which allows the model to learn groupings of species before learning the sometimes-subtle differences between similar species. Taxonomic training also allows the model to benefit from expert information about the taxonomic relationships between different species. We also found that random low-pass filtering was helpful for simulating distant sounds during training: As an audio source gets further away, the high-frequency parts fade away before the low-frequency parts. This was particularly effective for identifying species from the High Sierras region, where birdsongs cover very long distances, unimpeded by trees.

Classifying Separated Audio
We found that separating audio with the new MixIT model before classification improved the classifier performance on three independent real-world datasets. The separation was particularly successful for identification of quiet and background birds, and in many cases helped with overlapping vocalizations as well.

Top: A mel-spectrogram of two birds, an American pipit (amepip) and gray-crowned rosy finch (gcrfin), from the Sierra Nevadas. The legend shows the log-probabilities for the two species given by the pre-trained classifiers. Higher values indicate more confidence, and values greater than -1.0 are usually correct classifications. Bottom: A mel-spectrogram for the automatically separated audio, with the classifier log probabilities from the separated channels. Note that the classifier only identifies the gcrfin once the audio is separated.
Top: A complex mixture with three vocalizations: A golden-crowned kinglet (gockin), mountain chickadee (mouchi), and Steller’s jay (stejay). Bottom: Separation into three channels, with classifier log probabilities for the three species. We see good visual separation of the Steller’s jay (shown by the distinct pink marks), even though the classifier isn’t sure what it is.

The separation model does have some potential limitations. Occasionally we observe over-separation, where a single song is broken into multiple channels, which can cause misclassifications. We also notice that when multiple birds are vocalizing, the most prominent song often gets a lower score after separation. This may be due to loss of environmental context or other artifacts introduced by separation that do not appear during classifier training. For now, we get the best results by running the classifier on the separated channels and the original audio, and taking the maximum score for each species. We expect that further work will allow us to reduce over-separation and find better ways to combine separation and classification. You can see and hear more examples of the full system at our GitHub repo.

Future Directions
We are currently working with partners at the California Academy of Sciences to understand how habitat and species mix changes after prescribed fires and wildfires, applying these models to ARU audio collected over many years.

We also foresee many potential applications for the unsupervised separation models in ecology, beyond just birds. For example, the separated audio can be used to create better acoustic indices, which could measure ecosystem health by tracking the total activity of birds, insects, and amphibians without identifying particular species. Similar methods could also be adapted for use underwater to track coral reef health.

Acknowledgements
We would like to thank Mary Clapp, Jack Dumbacher, and Durrell Kapan from the California Academy of Sciences for providing extensive annotated soundscapes from the Sierra Nevadas. Stefan Kahl and Holger Klinck from the Cornell Lab of Ornithology provided soundscapes from Sapsucker Woods. Training data for both the separation and classification models came from Xeno-Canto and the Macaulay Library. Finally, we would like to thank Julie Cattiau, Lauren Harrell, Matt Harvey, and our co-author, John Hershey, from the Google Bioacoustics and Sound Separation teams.

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Meta Works with NVIDIA to Build Massive AI Research Supercomputer

Meta Platforms gave a big thumbs up to NVIDIA, choosing our technologies for what it believes will be its most powerful research system to date. The AI Research SuperCluster (RSC), announced today, is already training new models to advance AI. Once fully deployed, Meta’s RSC is expected to be the largest customer installation of NVIDIA Read article >

The post Meta Works with NVIDIA to Build Massive AI Research Supercomputer appeared first on The Official NVIDIA Blog.

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How the Intelligent Supply Chain Broke and AI Is Fixing It

Let’s face it, the global supply chain may not be the most scintillating subject matter. Yet in homes and businesses around the world, it’s quickly become the topic du jour: empty shelves; record price increases; clogged ports and sick truckers leading to disruptions near and far. The business of organizing resources to supply a product Read article >

The post How the Intelligent Supply Chain Broke and AI Is Fixing It appeared first on The Official NVIDIA Blog.

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Brain Tumor Segmentation and Classification using ResUnet

Brain Tumor Segmentation and Classification using ResUnet submitted by /u/Sudo_Python
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How do I slove this error?

I was doing an exercise by google dev’s ml tensorflow course. Im getting this error:

File “c:UsersshivaDocumentsAI_ML_TensorflowTensorflowEx2MNISTComputerVision.py“, line 24, in <module>

model.fit(x_train, y_train, epochs=5)

TypeError: Expected uint8, but got 1e-07 of type ‘float’.

———————————————————————————————————————————————

Here is the code:

# YOUR CODE SHOULD START HERE
# YOUR CODE SHOULD END HERE
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
# YOUR CODE SHOULD START HERE
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),

tf.keras.layers.Dense(10, activation=tf.nn.softmax)])
# YOUR CODE SHOULD END HERE
model = tf.keras.models.Sequential([
# YOUR CODE SHOULD START HERE

# YOUR CODE SHOULD END HERE
])
model.compile(optimizer = tf.keras.optimizers.Adam(),
loss = ‘sparse_categorical_crossentropy’,
metrics=[‘accuracy’])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
# YOUR CODE SHOULD START HERE
# YOUR CODE SHOULD END HERE

———————————————————————————————————————————————

I dunno what shud I do? I checked my code but can’t find anything that might cause the error. I asked the same question on the r/learnmachinelearning but got no response, Pls help!

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Any open source TensorFlow training orchestrator /dashboard?

hello r/tensorflow. As a backend engineer, I am very unfamiliar with tensorflow and ML in general, so please forgive me if this question seems unreasonable to you.

Because of the need of my lab, I’ve been looking for a solution for tensorflow orchestration. We have one server with a powerful GPU, and several users who want to run their tensorflow jobs on that powerful GPU. Instead of making schedules offline and individually log in to the server, is there any open source project I can deploy to the server that serves as an orchestrator?

For example, it provides a simple WebUI to let the user upload their job and all necessary files. Then the user submits the job to add it to a queue, which will run when it’s the first in the line. It will also report the progress and the result of the job.

I think there should be some kind of open-sourced project out there that fits this need, but I haven’t found it yet. So please help.

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How to disable exceptions encountered when calling Lambda layer?

I’m experimenting with some logic before creating a custom keras layer, but my Lambda layer isn’t allowing me to check the output shape with model.summary(). It says:

ValueError: Exception encountered when calling layer “Lambda_1” (type Lambda).

The following Variables were created within a Lambda layer (Lambda_1)

but are not tracked by said layer:

<tf.Variable ‘Lambda_1/map/while/RGAT_1/edge_type_0/kernel:0’ shape=(7, 10) dtype=float32>

<tf.Variable ‘Lambda_1/map/while/RGAT_1/edge_type_0/Edge_attention_parameters_0:0’ shape=(5, 4) dtype=float32>

The layer cannot safely ensure proper Variable reuse across multiple

calls, and consquently this behavior is disallowed for safety. Lambda

layers are not well suited to stateful computation; instead, writing a

subclassed Layer is the recommend way to define layers with

Variables.

Is there a way to temporally disable this behavior? 🤔

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Could you help to combine input layers with a specific NamedTuple class?

Hello, I’ve been searching/reading for a fair amount of hours, but I’m pretty much stuck with this problem.

This is my code:

from typing import NamedTuple class MessagePassingInput(NamedTuple): node_embeddings: tf.Tensor adjacency_lists: Tuple[tf.Tensor, ...] from keras import Model, layers import tensorflow as tf inputLayer_X = layers.Input(shape=tf.TensorShape(dims=(None, 7)),name="Input_X") inputLayer_A1 = layers.Input(shape=tf.TensorShape(dims=(None, 2)),name="Input_A1", dtype=tf.int32) inputLayer_A2 = layers.Input(shape=tf.TensorShape(dims=(None, 2)),name="Input_A2", dtype=tf.int32) inputLayer_A3 = layers.Input(shape=tf.TensorShape(dims=(None, 2)),name="Input_A3", dtype=tf.int32) 

And I would like that every entry in those inputs ends up in a next layer more or less like this: newLayer = [MessagePassingInput(inputLayer_X[i], [inputLayer_A1[i], inputLayer_A2[i], inputLayer_A3[i]]) for i in range(len(inputLayer_X))]. However, I’m just not being able to find how (I have tried with tf.map_fn and layers.Lambda, but wasn’t able to feed all those input layers and use the function in order)

If you could help me, I would be very grateful 🙏

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The Official Feedback and Discussion Thread

Here you can discuss anything that doesn’t require its own post

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LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything

Language models are becoming more capable than ever before and are helpful in a variety of tasks — translating one language into another, summarizing a long document into a brief highlight, or answering information-seeking questions. Among these, open-domain dialog, where a model needs to be able to converse about any topic, is probably one of the most difficult, with a wide range of potential applications and open challenges. In addition to producing responses that humans judge as sensible, interesting, and specific to the context, dialog models should adhere to Responsible AI practices, and avoid making factual statements that are not supported by external information sources.

Today we’re excited to share recent advances in our “LaMDA: Language Models for Dialog Applications” project. In this post, we’ll give an overview on how we’re making progress towards safe, grounded, and high-quality dialog applications. LaMDA is built by fine-tuning a family of Transformer-based neural language models specialized for dialog, with up to 137B model parameters, and teaching the models to leverage external knowledge sources.

Objectives & Metrics
Defining objectives and metrics is critical to guide training dialog models. LaMDA has three key objectives — Quality, Safety, and Groundedness — each of which we measure using carefully designed metrics:

Quality: We decompose Quality into three dimensions, Sensibleness, Specificity, and Interestingness (SSI), which are evaluated by human raters. Sensibleness refers to whether the model produces responses that make sense in the dialog context (e.g., no common sense mistakes, no absurd responses, and no contradictions with earlier responses). Specificity is measured by judging whether the system’s response is specific to the preceding dialog context, and not a generic response that could apply to most contexts (e.g., “ok” or “I don’t know”). Finally, Interestingness measures whether the model produces responses that are also insightful, unexpected or witty, and are therefore more likely to create better dialog.

Safety: We’re also making progress towards addressing important questions related to the development and deployment of Responsible AI. Our Safety metric is composed of an illustrative set of safety objectives that captures the behavior that the model should exhibit in a dialog. These objectives attempt to constrain the model’s output to avoid any unintended results that create risks of harm for the user, and to avoid reinforcing unfair bias. For example, these objectives train the model to avoid producing outputs that contain violent or gory content, promote slurs or hateful stereotypes towards groups of people, or contain profanity. Our research towards developing a practical Safety metric represents very early work, and there is still a great deal of progress for us to make in this area.

Groundedness: The current generation of language models often generate statements that seem plausible, but actually contradict facts established in known external sources. This motivates our study of groundedness in LaMDA. Groundedness is defined as the percentage of responses with claims about the external world that can be supported by authoritative external sources, as a share of all responses containing claims about the external world. A related metric, Informativeness, is defined as the percentage of responses with information about the external world that can be supported by known sources, as a share of all responses. Therefore, casual responses that do not carry any real world information (e.g., “That’s a great idea”), affect Informativeness but not Groundedness. While grounding LaMDA generated responses in known sources does not in itself guarantee factual accuracy, it allows users or external systems to judge the validity of a response based on the reliability of its source.

LaMDA Pre-Training
With the objectives and metrics defined, we describe LaMDA’s two-stage training: pre-training and fine-tuning. In the pre-training stage, we first created a dataset of 1.56T words — nearly 40 times more words than what were used to train previous dialog models — from public dialog data and other public web documents. After tokenizing the dataset into 2.81T SentencePiece tokens, we pre-train the model using GSPMD to predict every next token in a sentence, given the previous tokens. The pre-trained LaMDA model has also been widely used for natural language processing research across Google, including program synthesis, zero-shot learning, style transfer, as well as in the BIG-bench workshop.

LaMDA Fine-Tuning
In the fine-tuning stage, we train LaMDA to perform a mix of generative tasks to generate natural-language responses to given contexts, and classification tasks on whether a response is safe and high-quality, resulting in a single multi-task model that can do both. The LaMDA generator is trained to predict the next token on a dialog dataset restricted to back-and-forth dialog between two authors, while the LaMDA classifiers are trained to predict the Safety and Quality (SSI) ratings for the response in context using annotated data. During a dialog, the LaMDA generator first generates several candidate responses given the current multi-turn dialog context, and the LaMDA classifiers predict the SSI and Safety scores for every response candidate. Candidate responses with low Safety scores are first filtered out. Remaining candidates are re-ranked by their SSI scores, and the top result is selected as the response. We further filter the training data used for the generation task with LaMDA classifiers to increase the density of high-quality response candidates.

LaMDA generates and then scores a response candidate.
LaMDA handles arbitrary user input in a way that is sensible, specific, and interesting. Only LaMDA’s very first statement “Hello, I’m a friendly…” was hard coded to set the purpose of the dialog.

Factual Grounding
While people are capable of checking their facts by using tools and referencing established knowledge bases, many language models draw their knowledge on their internal model parameters only. To improve the groundedness of LaMDA’s original response, we collect a dataset of dialogs between people and LaMDA, which are annotated with information retrieval queries and the retrieved results where applicable. We then fine-tune LaMDA’s generator and classifier on this dataset to learn to call an external information retrieval system during its interaction with the user to improve the groundedness of its responses. While this is very early work, we’re seeing promising results.

Zero-shot domain adaptation: cherry-picked, but real example of LaMDA pretending to be Mount Everest, by simply setting its initial message to be “Hi I’m Mount Everest. What would you like me to know about me?” Everest LaMDA is shown providing educational and factually correct responses.

Evaluation
In order to quantify progress against our key metrics, we collect responses from the pre-trained model, fine-tuned model, and human raters (i.e., human-generated responses) to multi-turn two-author dialogs, and then ask a different set of human raters a series of questions to evaluate these responses against the Quality, Safety, and Groundedness metrics.

We observe that LaMDA significantly outperforms the pre-trained model in every dimension and across all model sizes. Quality metrics (Sensibleness, Specificity, and Interestingness, in the first column below) generally improve with the number of model parameters, with or without fine-tuning. Safety does not seem to benefit from model scaling alone, but it does improve with fine-tuning. Groundedness improves as model size increases, perhaps because larger models have a greater capacity to memorize uncommon knowledge, but fine-tuning allows the model to access external knowledge sources and effectively shift some of the load of remembering knowledge to an external knowledge source. With fine-tuning, the quality gap to human levels can be narrowed, though the model’s performance remains below human levels in safety and groundedness.

Comparing the pre-trained model (PT), fine-tuned model (LaMDA) and human-rater-generated dialogs (Human) across Sensibleness, Specificity, Interestingness, Safety, Groundedness, and Informativeness. The test sets used to measure Safety and Groundedness were designed to be especially difficult.

Future Research & Challenges
LaMDA’s level of Sensibleness, Specificity and Interestingness unlocks new avenues for understanding the benefits and risks of open-ended dialog agents. It also presents encouraging evidence that key challenges with neural language models, such as using a safety metric and improving groundedness, can improve with larger models and fine-tuning with more well-labeled data. However, this is very early work, and there are significant limitations. Exploring new ways to improve our Safety metric and LaMDA’s groundedness, aligned with our AI Principles, will continue to be our main areas of focus going forward.

Acknowledgements
We’d to like to thank everyone for contributing to the project and paper, including: Blaise Aguera-Arcas, Javier Alberca, Thushan Amarasiriwardena, Lora Aroyo, Martin Baeuml, Leslie Baker, Rachel Bernstein, Taylor Bos, Maarten Bosma, Jonas Bragagnolo, Alena Butryna, Bill Byrne, Chung-Ching Chang, Zhifeng Chen, Dehao Chen, Heng-Tze Cheng, Ed Chi, Aaron Cohen, Eli Collins, Marian Croak, Claire Cui, Andrew Dai, Dipanjan Das, Daniel De Freitas, Jeff Dean, Rajat Dewan, Mark Diaz, Tulsee Doshi, Yu Du, Toju Duke, Doug Eck, Joe Fenton, Noah Fiedel, Christian Frueh, Harish Ganapathy, Saravanan Ganesh, Amin Ghafouri, Zoubin Ghahramani, Kourosh Gharachorloo, Jamie Hall, Erin Hoffman-John, Sissie Hsiao, Yanping Huang, Ben Hutchinson, Daphne Ippolito, Alicia Jin, Thomas Jurdi, Ashwin Kakarla, Nand Kishore, Maxim Krikun, Karthik Krishnamoorthi, Igor Krivokon, Apoorv Kulshreshtha, Ray Kurzweil, Viktoriya Kuzmina, Vivek Kwatra, Matthew Lamm, Quoc Le, Max Lee, Katherine Lee, Hongrae Lee, Josh Lee, Dmitry Lepikhin, YaGuang Li, Yifeng Lu, David Luan, Daphne Luong, Laichee Man, Jianchang (JC) Mao, Yossi Matias, Kathleen Meier-Hellstern, Marcelo Menegali, Muqthar Mohammad,, Muqthar Mohammad, Alejandra Molina, Erica Moreira, Meredith Ringel Morris, Maysam Moussalem, Jiaqi Mu, Tyler Mullen, Tyler Mullen, Eric Ni, Kristen Olson, Alexander Passos, Fernando Pereira, Slav Petrov, Marc Pickett, Roberto Pieraccini, Christian Plagemann, Sahitya Potluri, Vinodkumar Prabhakaran, Andy Pratt, James Qin, Ravi Rajakumar, Adam Roberts, Will Rusch, Renelito Delos Santos, Noam Shazeer, RJ Skerry-Ryan, Grigori Somin, Johnny Soraker, Pranesh Srinivasan, Amarnag Subramanya, Mustafa Suleyman, Romal Thoppilan, Song Wang, Sheng Wang, Chris Wassman, Yuanzhong Xu, Yuanzhong Xu, Ni Yan, Ben Zevenbergen, Vincent Zhao, Huaixiu Steven Zheng, Denny Zhou, Hao Zhou, Yanqi Zhou, and more.