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diff --git a/src/main/java/org/apache/commons/math3/transform/DftNormalization.java b/src/main/java/org/apache/commons/math3/transform/DftNormalization.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.commons.math3.transform;
+
+/**
+ * This enumeration defines the various types of normalizations that can be applied to discrete
+ * Fourier transforms (DFT). The exact definition of these normalizations is detailed below.
+ *
+ * @see FastFourierTransformer
+ * @since 3.0
+ */
+public enum DftNormalization {
+    /**
+     * Should be passed to the constructor of {@link FastFourierTransformer} to use the
+     * <em>standard</em> normalization convention. This normalization convention is defined as
+     * follows
+     *
+     * <ul>
+     *   <li>forward transform: y<sub>n</sub> = &sum;<sub>k=0</sub><sup>N-1</sup> x<sub>k</sub>
+     *       exp(-2&pi;i n k / N),
+     *   <li>inverse transform: x<sub>k</sub> = N<sup>-1</sup> &sum;<sub>n=0</sub><sup>N-1</sup>
+     *       y<sub>n</sub> exp(2&pi;i n k / N),
+     * </ul>
+     *
+     * where N is the size of the data sample.
+     */
+    STANDARD,
+
+    /**
+     * Should be passed to the constructor of {@link FastFourierTransformer} to use the
+     * <em>unitary</em> normalization convention. This normalization convention is defined as
+     * follows
+     *
+     * <ul>
+     *   <li>forward transform: y<sub>n</sub> = (1 / &radic;N) &sum;<sub>k=0</sub><sup>N-1</sup>
+     *       x<sub>k</sub> exp(-2&pi;i n k / N),
+     *   <li>inverse transform: x<sub>k</sub> = (1 / &radic;N) &sum;<sub>n=0</sub><sup>N-1</sup>
+     *       y<sub>n</sub> exp(2&pi;i n k / N),
+     * </ul>
+     *
+     * which makes the transform unitary. N is the size of the data sample.
+     */
+    UNITARY;
+}